% Encoding: UTF-8
@misc{Deng1993,
author = {Deng, G and Cahill, L W},
booktitle = {1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference},
doi = {10.1109/NSSMIC.1993.373563},
isbn = {VO  -},
keywords = {Adaptive filters,Computer vision,Digital filters,Distortion,Filtering algorithms,Gaussian noise,Image edge detection,Image processing,Imaging phantoms,Noise reduction,adaptive Gaussian filter,adaptive filters,computer vision,edge detection,edge position displacement,image processing,noise reduction,noise suppression,phantom edges,preprocessing,signal local variance,vanishing edges},
pages = {1615--1619 vol.3},
title = {{An adaptive Gaussian filter for noise reduction and edge detection}},
year = {1993}
}
@inproceedings{Prasad2011,
abstract = {We prove that when a line is approximated using digital line, the error in the slope of the digital line has a definite upper bound and is strongly dependent on the two pixels chosen for defining the digital line. Thus, an analytical expression of the maximum deviation of the pixels from the digital line can be derived. Using this, the conventional line fitting methods that use maximum tolerable deviation as the optimization goal can be made control-parameter independent. This error bound can be used to make the most recent and sophisticated line fitting methods parameter independent and more robust to digitization noises. In our knowledge, this is the first line fitting method completely devoid of any control parameter. Such control- parameter independent line fitting algorithm retains the characteristics of the digital curve with sufficient reliability and precision and provides good dimensionality reduction in representing the digital curves. Extensive results have been generated for 9 datasets comprising of about a hundred thousand images. The proposed method shows robust and repeatable performance across all the datasets with low standard deviation in the performance.},
author = {Prasad, Dilip K. and Quek, Chai and Leung, Maylor K.H. and Cho, Siu Yeung},
booktitle = {1st Asian Conference on Pattern Recognition, ACPR 2011},
doi = {10.1109/ACPR.2011.6166585},
isbn = {9781457701221},
keywords = {digitization,dominant points,line fitting},
pages = {441--445},
title = {{A parameter independent line fitting method}},
year = {2011}
}
@article{Tan2007,
abstract = {Bad weather, particularly fog and haze, commonly obstruct drivers from observing road conditions. This could frequently lead to a considerable number of road accidents. To avoid the problem, automatic methods have been proposed to enhance visibility in bad weather. Methods that work on visible wavelengths, based on the type of their input, can be categorized into two approaches: those using polarizing filters, and those using images taken from different fog densities. Both of the approaches require that the images are multiple and taken from exactly the same point of view. While they can produce reasonably good results, their requirement makes them impractical, particularly in real time applications, such as vehicle systems. Considering their drawbacks, our goal is to develop a method that requires solely a single image taken from ordinary digital cameras, without any additional hardware. The method principally uses color and intensity information. It enhances the visibility after estimating the color of skylight and the values of airlight. The experimental results on real images show the effectiveness of the approach.},
author = {Tan, Robby T. and Pettersson, Niklas and Petersson, Lars},
doi = {10.1109/IVS.2007.4290085},
isbn = {1-4244-1067-3},
issn = {1931-0587},
journal = {2007 IEEE Intelligent Vehicles Symposium},
pages = {19--24},
title = {{Visibility Enhancement for Roads with Foggy or Hazy Scenes}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4290085},
year = {2007}
}
@article{Mateer2011,
abstract = {It is well known that the convolution of two signals is equivalent to the multiplication of two polynomials. Fast polynomial multiplication can be implemented by evaluating the two input polynomials at a collection of points, multiplying the polynomial evaluations, and then interpolating the results into the product polynomial. In 2004 and 2005, van der Hoeven introduced algorithms which efficiently evaluate and interpolate a polynomial at certain collections of n {\&}38;60; N points which are roots of xN {\&}x2212; 1 where N is a power of two. A finite field of characteristic two does not have the necessary properties to use this algorithm. In this paper, we present a generalization of the van der Hoeven algorithms which can also be applied to the finite field case. The generalized algorithms also improve van der Hoeven's presentation to take advantage of a slightly more efficient factorization due to Crandall and Fagin. The evaluation and interpolation problems will be viewed in terms of polynomial rings, building upon previous work by Fiduccia, Martens, and Bernstein.},
author = {Mateer, Todd D.},
doi = {10.1109/DSP-SPE.2011.5739190},
file = {::},
isbn = {9781612842271},
journal = {2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings},
keywords = {FFT,Fourier transform,convolution,interpolation,polynomial multiplication},
number = {August},
pages = {78--83},
title = {{Truncated Fast Fourier Transform algorithms}},
year = {2011}
}
@inproceedings{Jeong2013,
abstract = {In this paper, we propose a novel method for estimating the transmission and removing the haze from single image. This method is based on observation that the property of haze is widely spread. Thus its estimated transmission should be smoothly changed over the scene. We employed the local entropy and log function for estimating the atmospheric light and the smooth transmission. Experimental results demonstrated that the proposed method can be applied efficiently to outdoor vision applications or devices including cameras and camcorders with low complexity.},
author = {Jeong, S and Lee, S},
booktitle = {2013 IEEE International Conference on Consumer Electronics (ICCE)},
doi = {10.1109/ICCE.2013.6486936},
isbn = {2158-3994 VO  -},
keywords = {Channel estimation,Complexity theory,Computer vision,Conferences,Educational institutions,Entropy,Estimation,atmospheric light,camcorders,cameras,efficient transmission estimation,entropy,image restoration,local entropy,log function,outdoor vision applications,single image dehazing,smooth transmission},
pages = {376--377},
title = {{The single image dehazing based on efficient transmission estimation}},
year = {2013}
}
@article{6738147,
address = {Melbourne, Canada},
author = {Diego, U C San and Jolla, La and Gibson, Kristofor B and Nguyen, Truong Q},
doi = {10.1109/ICIP.2013.6738147},
file = {::;::},
isbn = {9781479923410},
journal = {2013 IEEE International Conference on Image Processing},
keywords = {Wiener filters,adaptive filters,fog,image denoisin},
month = {sep},
number = {2},
pages = {714--718},
publisher = {IEEE},
title = {{FAST SINGLE IMAGE FOG REMOVAL USING THE ADAPTIVE WIENER FILTER Kristofor B . Gibson and Truong Q . Nguyen}},
url = {http://ieeexplore.ieee.org/document/6738147/},
volume = {1},
year = {2013}
}
@inproceedings{Dantas2016,
author = {Dantas, Daniel Oliveira and {Danilo Passos Leal}, Helton and {Barros Sousa}, Davy Oliveira},
booktitle = {2016 IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2016.7532664},
isbn = {978-1-4673-9961-6},
month = {sep},
pages = {1779--1783},
publisher = {IEEE},
title = {{Fast multidimensional image processing with OpenCL}},
url = {http://ieeexplore.ieee.org/document/7532664/},
year = {2016}
}
@inproceedings{39277323,
address = {New York, USA},
author = {Hu, Huaifei and Liu, Hong and Chen, Lili and Hung, Chih-Cheng and Xu, Xiangyang and Lan, Zhicong},
booktitle = {ACM Symposium on Applied Computing (SAC)},
doi = {10.1145/1982185.1982370},
isbn = {978-1-4503-0113-8},
keywords = {computer-aided diagnosis,fitting weight matrix,rotating parabolic fitting,segmentation of cervical vertebra,x-ray images},
month = {mar},
pages = {853--858},
title = {{Image Segmentation of Cervical Vertebra in X-ray Radiographs Using the Curve Fitting Strategy}},
volume = {1},
year = {2011}
}
@article{Fattal2014,
address = {New York, NY, USA},
author = {Fattal, Raanan},
doi = {10.1145/2651362},
file = {::},
issn = {0730-0301},
journal = {ACM Transactions on Graphics (TOG)},
keywords = {Image dehazing,contrast enhancement,transmission estimation},
number = {1},
pages = {13:1----13:14},
publisher = {ACM},
title = {{Dehazing Using Color-Lines}},
volume = {34},
year = {2014}
}
@article{kopf2008deep,
address = {New York, NY, USA},
author = {Kopf, Johannes and Neubert, Boris and Chen, Billy and Cohen, Michael and Cohen-Or, Daniel and Deussen, Oliver and Uyttendaele, Matt and Lischinski, Dani},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kopf et al. - 2008 - Deep Photo Model-based Photograph Enhancement and Viewing.pdf:pdf;:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kopf et al. - 2008 - Deep Photo Model-based Photograph Enhancement and Viewing(2).pdf:pdf},
issn = {0730-0301},
journal = {ACM Transactions on Graphics (TOG)},
keywords = {dehazing,image completion,image-based modeling,image-based rendering,photo browsing,relighting},
number = {5},
pages = {116:1----116:10},
publisher = {ACM},
title = {{Deep Photo: Model-based Photograph Enhancement and Viewing}},
volume = {27},
year = {2008}
}
@inproceedings{fattal2008single,
address = {New York, USA},
author = {Fattal, Raanan},
booktitle = {ACM Transactions on Graphics (TOG)},
doi = {10.1145/1360612.1360671},
file = {::},
issn = {0730-0301},
keywords = {Markov random field image modeling,computational photography,image dehazing/defogging,image enhancement,image restoration},
month = {aug},
number = {3},
pages = {72:1----72:9},
title = {{Single Image Dehazing}},
volume = {27},
year = {2008}
}
@article{kim2013scene,
address = {New York, NY, USA},
author = {Kim, Changil and Zimmer, Henning and Pritch, Yael and Sorkine-Hornung, Alexander and Gross, Markus},
issn = {0730-0301},
journal = {ACM Transactions on Graphics (TOG)},
keywords = {image-based scene reconstruction,light fields},
number = {4},
pages = {73:1----73:12},
publisher = {ACM},
title = {{Scene Reconstruction from High Spatio-angular Resolution Light Fields}},
volume = {32},
year = {2013}
}
@article{lei2011improved,
author = {Lei, W D and Li, K and Wang, X P},
journal = {Advanced Materials Research},
pages = {1734--1737},
publisher = {Trans Tech Publ},
title = {{Improved Watershed Segmentation Method in Rock Fragmentation Analysis on Digital Photos}},
volume = {261},
year = {2011}
}
@inproceedings{Cheng2016,
author = {Cheng, Yue and Niu, Wensheng and Zhai, Zhengjun},
booktitle = {AIAA/IEEE Digital Avionics Systems Conference - Proceedings},
doi = {10.1109/DASC.2016.7777960},
isbn = {9781509056002},
issn = {21557209},
keywords = {JRBF,Multi-core,OpenMP,Real-time Dehazing},
title = {{Video dehazing for surveillance unmanned aerial vehicle}},
volume = {2016-Decem},
year = {2016}
}
@inproceedings{hacmec,
address = {Tokyo, Japan},
author = {{Jessica El Khoury Jean-Baptiste Thomas}, Alamin Mansouri},
booktitle = {AIC 2015},
month = {may},
pages = {1--7},
title = {{HAZE AND CONVERGENCE MODELS: EXPERIMENTAL COMPARISON}},
volume = {1},
year = {2015}
}
@article{On2015,
author = {On, Divisi},
file = {::},
journal = {Aic 2015},
pages = {1--7},
title = {{CAMPUS IRAPUATO - SALAMANCA la profundidad de campo en im ´}},
volume = {1},
year = {2015}
}
@article{xie2011liver,
author = {Xie, X H and Ma, C and Yu, X F and Du, R X},
journal = {Applied Mechanics and Materials},
pages = {1311--1316},
publisher = {Trans Tech Publ},
title = {{Liver Image Segmentation Using Improved Watershed Method}},
volume = {58},
year = {2011}
}
@article{ronhdabodcpficbu,
author = {Xu, Bei Bei and Li, Jie and Guan, Zhen Yu and Yang, Huan and Zhang, He},
doi = {10.4028/www.scientific.net/AMM.496-500.1853},
file = {::},
isbn = {9783037859926},
issn = {1662-7482},
journal = {Applied Mechanics and Materials},
keywords = {a,abstract,algorithm firstly uses the,dark,dark channel prior,edge detection,for the uav attitude,horizon detection,horizon detection algorithm based,is proposed,measurement based on the,on the dark channel,principle of dark channel,prior for the haze,prior in foggy weather,removal to get the,this,to detect the horizon,uav,visual navigation},
number = {1},
pages = {1853--1856},
title = {{Research on Horizon Detection Algorithm Based on Dark Channel Prior for Images Captured by UAV}},
url = {http://www.scientific.net/AMM.496-500.1853},
volume = {496-500},
year = {2014}
}
@article{zhao2004research,
author = {Zhao, Hong and WANG, Li-min and WANG, Gong-yi},
journal = {Applied Science and technology},
pages = {6},
title = {{Research on binarization method of license plate recognition in automatic recognition [J]}},
volume = {3},
year = {2004}
}
@article{brice-fennema:1970a,
author = {Brice, C R and Fennema, C L},
journal = {Artificial intelligence},
number = {3-4},
pages = {205--226},
publisher = {Elsevier},
title = {{Scene analysis using regions}},
volume = {1},
year = {1970}
}
@article{Gardner1998,
abstract = {Artificial neural networks are appearing as useful alternatives to traditional statistical modelling techniques in many scientific disciplines. This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences.},
author = {Gardner, M.W and Dorling, S.R},
doi = {10.1016/S1352-2310(97)00447-0},
isbn = {1352-2310},
issn = {13522310},
journal = {Atmospheric Environment},
keywords = {Statistical modelling,artificial intelligence,backpropagation,neural network},
number = {14-15},
pages = {2627--2636},
title = {{Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences}},
url = {http://www.sciencedirect.com/science/article/pii/S1352231097004470},
volume = {32},
year = {1998}
}
@inproceedings{4181632,
address = {Coventry ,UK},
author = {Malisiewicz, Tomasz and Efros, Alexei A},
booktitle = {British Machine Vision Conference (BMVC)},
month = {sep},
title = {{Improving Spatial Support for Objects via Multiple Segmentations}},
volume = {1},
year = {2007}
}
@article{Saalfeld1999,
abstract = {We examine key properties of the Douglas-Peucker polyline simplification algorithm which are shared with many similar "vertex sub-sampling" algorithms. We examine how the Douglas-Peucker algorithm and similar algorithms can fail to maintain consistent or correct topological relations among features. We then prove that a simple test added to the stopping condition of Douglas-Peucker-like algorithms can guarantee that the resulting simplified polyline is topologically consistent with itself and with all of its neighboring features, and is correctly situated topologically with respect to all other features. We describe how a dynamically updated convex hull data structure may be used to efficiently detect and remove potential topological conflicts of the polyline with itself and with other features in that polyline's neighborhood.},
author = {Saalfeld, Alan},
doi = {10.1559/152304099782424901},
isbn = {1523-0406},
issn = {1523-0406},
journal = {Cartography and Geographic Information Science},
number = {1},
pages = {7--18},
title = {{Topologically Consistent Line Simplification with the Douglas-Peucker Algorithm}},
url = {http://www.tandfonline.com/doi/abs/10.1559/152304099782424901},
volume = {26},
year = {1999}
}
@article{wu2011infrared,
author = {WU, Yun and PENG, Zhen-ming},
journal = {Command Control {\&} Simulation},
pages = {23},
title = {{Infrared Image Segmentation Based on 2D Otsu with Fractal Constraining}},
volume = {2},
year = {2011}
}
@article{garcia2011contribucion,
author = {GARC{\'{I}}A, N and ALFONZO, D and Others},
journal = {Compartir},
pages = {17},
title = {{Contribuci{\{}{\'{o}}{\}}n a la autolocalizaci{\{}{\'{o}}{\}}n de robots m{\{}{\'{o}}{\}}viles basada en la fusi{\{}{\'{o}}{\}}n de informaci{\{}{\'{o}}{\}}n multisensorial.}},
volume = {3304},
year = {2011}
}
@article{wang2011modified,
author = {Wang, Hanzheng and Moss, Randy H and Chen, Xiaohe and Stanley, R Joe and Stoecker, William V and Celebi, M Emre and Malters, Joseph M and Grichnik, James M and Marghoob, Ashfaq A and Rabinovitz, Harold S and Menzies, Scott W and Szalapski, Thomas M},
doi = {10.1016/j.compmedimag.2010.09.006},
journal = {Comput Med Imaging Graph},
number = {2},
pages = {116--120},
title = {{Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images}},
volume = {35},
year = {2011}
}
@inproceedings{5693028,
address = {Hangzhou, China},
author = {Lv, Xingyong and Chen, Wenbin and Shen, I-Fan},
booktitle = {Computer Graphics and Applications (PG), 2010 18th Pacific Conference on},
doi = {10.1109/PacificGraphics.2010.16},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lv, Chen, Shen - 2010 - Real-Time Dehazing for Image and Video.pdf:pdf},
keywords = {filtering theory;image reconstruction;video signal},
month = {sep},
pages = {62--69},
title = {{Real-Time Dehazing for Image and Video}},
volume = {1},
year = {2010}
}
@incollection{Ren2016,
abstract = {The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes. In this paper, we propose a multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps. The proposed algorithm consists of a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale net which refines results locally. To train the multi-scale deep network, we synthesize a dataset comprised of hazy images and corresponding transmission maps based on the NYU Depth dataset. Extensive experiments demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods on both synthetic and real-world images in terms of quality and speed.},
address = {Cham},
author = {Ren, Wenqi and Liu, Si and Zhang, Hua and Pan, Jinshan and Cao, Xiaochun and Yang, Ming-Hsuan},
booktitle = {Computer Vision -- ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II},
doi = {10.1007/978-3-319-46475-6_10},
editor = {Leibe, Bastian and Matas, Jiri and Sebe, Nicu and Welling, Max},
file = {::},
isbn = {978-3-319-46475-6},
pages = {154--169},
publisher = {Springer International Publishing},
title = {{Single Image Dehazing via Multi-scale Convolutional Neural Networks}},
url = {https://doi.org/10.1007/978-3-319-46475-6{\_}10},
year = {2016}
}
@incollection{Ancuti2011,
author = {Ancuti, CodrutaO. and Ancuti, Cosmin and Hermans, Chris and Bekaert, Philippe},
booktitle = {Computer Vision (ACCV)},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ancuti et al. - 2011 - A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image.pdf:pdf},
pages = {501--514},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
title = {{A Fast Semi-inverse Approach to Detect and Remove the Haze from a Single Image}},
volume = {6493},
year = {2011}
}
@incollection{pantofaru2008object,
author = {Pantofaru, Caroline and Schmid, Cordelia and Hebert, Martial},
booktitle = {Computer Vision (ECCV)},
pages = {481--494},
publisher = {Springer},
title = {{Object recognition by integrating multiple image segmentations}},
year = {2008}
}
@article{HaraShap:85,
author = {Haralick, R M and Shapiro, L G},
journal = {Computer vision, graphics, and image processing},
number = {1},
pages = {100--132},
publisher = {Elsevier},
title = {{Image segmentation techniques}},
volume = {29},
year = {1985}
}
@article{KAPUR1985273,
author = {Kapur, J N and Sahoo, P K and Wong, A K C},
doi = {http://dx.doi.org/10.1016/0734-189X(85)90125-2},
issn = {0734-189X},
journal = {Computer Vision, Graphics, and Image Processing},
number = {3},
pages = {273--285},
title = {{A new method for gray-level picture thresholding using the entropy of the histogram}},
url = {http://www.sciencedirect.com/science/article/pii/0734189X85901252},
volume = {29},
year = {1985}
}
@article{815939,
address = {San Diego, CA, USA},
author = {Heimans, H J A M and Ronse, C},
doi = {10.1016/0734-189X(90)90148-O},
issn = {0734-189X},
journal = {Computer Vision, Graphics, and Image Processing},
number = {3},
pages = {245--295},
publisher = {Academic Press Professional, Inc.},
title = {{The Algebraic Basis of Mathematical Morphology. I. Dilations and Erosions}},
volume = {50},
year = {1990}
}
@book{series/sci/2010-285,
author = {Cipolla, R},
booktitle = {Computer Vision: Detection, Recognition and Reconstruction},
edition = {1st},
isbn = {978-3-642-12847-9},
publisher = {Springer},
series = {Studies in Computational Intelligence},
title = {{Computer Vision: Detection, Recognition and Reconstruction}},
volume = {285},
year = {2010}
}
@article{Serikawa201441,
annote = {40th-year commemorative issue},
author = {Serikawa, Seiichi and Lu, Huimin},
doi = {http://dx.doi.org/10.1016/j.compeleceng.2013.10.016},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Serikawa, Lu - 2014 - Underwater image dehazing using joint trilateral filter.pdf:pdf},
issn = {0045-7906},
journal = {Computers {\&} Electrical Engineering},
number = {1},
pages = {41--50},
title = {{Underwater image dehazing using joint trilateral filter}},
url = {http://www.sciencedirect.com/science/article/pii/S0045790613002644},
volume = {40},
year = {2014}
}
@article{lee1998unsupervised,
author = {Lee, Chulhee and Huh, Shin and Ketter, Terence A and Unser, Michael},
journal = {Computers in biology and medicine},
number = {3},
pages = {309--338},
publisher = {Elsevier},
title = {{Unsupervised connectivity-based thresholding segmentation of midsagittal brain MR images}},
volume = {28},
year = {1998}
}
@inproceedings{Martinez-Herrera2014,
abstract = {Induction Motors are subjected to unavoidable stresses that create failures in their different parts and result in substantial cost penalties. An effective incipient fault detection technique can reduce the maintenance expenses by preventing high cost failures and unscheduled downtimes. Broken rotor bars (BRB) is the most common rotor-related failure, and the startup transient is suitable for their detection. Therefore, several time-frequency representations have been proposed for this aim. This paper presents a performance comparison between the Gabor transform and the Wigner-Ville with a Choi-Williams kernel (WVD-CW) transform for broken rotor bar detection in induction motors. Preliminary results show that WVD-CW has better time-frequency resolution than the Gabor transform ensuring the BRB fault detection with high certainty.},
author = {Martinez-Herrera, Ana L. and Ledesma-Carrillo, Luis M. and Lopez-Ramirez, Misael and Salazar-Colores, Sebastian and Cabal-Yepez, Eduardo and Garcia-Perez, Arturo},
booktitle = {CONIELECOMP 2014 - 24th International Conference on Electronics, Communications and Computers},
keywords = {Gabor transform,Wigner-Ville,broken rotor bar,induction motors},
pages = {83--87},
publisher = {IEEE Computer Society},
title = {{Gabor and the Wigner-Ville transforms for broken rotor bars detection in induction motors}},
year = {2014}
}
@inproceedings{7162616,
address = {Qingdao, China},
author = {Chengtao, C and Qiuyu, Z and Yanhua, L},
booktitle = {Control and Decision Conference (CCDC)},
doi = {10.1109/CCDC.2015.7162616},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Chengtao, Qiuyu, Yanhua - 2015 - A survey of image dehazing approaches.pdf:pdf},
keywords = {image colour analysis;image enhancement;image rest},
month = {may},
pages = {3964--3969},
title = {{A survey of image dehazing approaches}},
volume = {1},
year = {2015}
}
@inproceedings{7161852,
address = {Qingdao, China},
author = {Song, Yingchao and Luo, Haibo and Hui, Bing and Chang, Zheng},
booktitle = {Control and Decision Conference (CCDC)},
doi = {10.1109/CCDC.2015.7161852},
file = {::},
keywords = {brightness;computational complexity;image colour a},
month = {may},
pages = {5840--5845},
title = {{An improved image dehazing and enhancing method using dark channel prior}},
volume = {1},
year = {2015}
}
@article{Lin2010,
author = {Lin, Jeng-feng and Kang, Chin-chieh and Tseng, Shih-fu},
file = {::},
journal = {Design},
keywords = {haze,optical film,paraboloidal microlens array},
pages = {5--7},
title = {{Analytical Haze Simulation of Optical Films with 2D Paraboloidal Microlens Array}},
year = {2010}
}
@article{Jeong2013a,
author = {Jeong, Soowoong and Lee, Sangkeun},
doi = {10.1109/ICCE.2013.6486936},
file = {::},
isbn = {9781467313612},
issn = {0747668X},
journal = {Digest of Technical Papers - IEEE International Conference on Consumer Electronics},
pages = {376--377},
title = {{The single image dehazing based on efficient transmission estimation}},
year = {2013}
}
@inproceedings{Carr2009,
address = {Melbourne, Canada},
author = {Carr, P and Hartley, R},
booktitle = {Digital Image Computing: Techniques and Applications (DICTA)},
doi = {10.1109/DICTA.2009.25},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Carr, Hartley - 2009 - Improved Single Image Dehazing Using Geometry.pdf:pdf},
keywords = {fog;image enhancement;image resolution;optimisatio},
month = {dec},
pages = {103--110},
title = {{Improved Single Image Dehazing Using Geometry}},
volume = {1},
year = {2009}
}
@inproceedings{movafeghiimprovement,
address = {Ghent, Belgium},
author = {Movafeghi, Amir and Yahaghi, Effat and Mohammadzadeh, Nouroddin and Rokrok, Berouz and Rastkhah, Nasser},
booktitle = {Digital Industrial Radiology and Computed Tomography (DIR 2015)},
file = {::},
month = {jun},
pages = {1--5},
title = {{Improvement of radiography images contrast and detection of the cracks in welds by dehazing algorithm}},
volume = {1},
year = {2015}
}
@inproceedings{6900715,
address = {Hong Kong, China},
author = {Hu, Xiaochen and Zhuo, Li and Li, Xiaoguang},
booktitle = {Digital Signal Processing (DSP), 2014 19th International Conference on},
doi = {10.1109/ICDSP.2014.6900715},
file = {::},
keywords = {computational complexity;computerised monitoring;i},
month = {aug},
pages = {1--6},
title = {{A moving objects based real-time defogging method for traffic monitoring videos}},
volume = {1},
year = {2014}
}
@article{4550695,
author = {Huynh-Thu, Q and Ghanbari, M},
doi = {10.1049/el:20080522},
issn = {0013-5194},
journal = {Electronics Letters},
keywords = {video codecs;video signal processing;PSNR;codec;im},
number = {13},
pages = {800--801},
title = {{Scope of validity of PSNR in image/video quality assessment}},
volume = {44},
year = {2008}
}
@article{Bui2014,
abstract = {In image dehazing, the existing transmission estimators bring out the halo artefact at boundaries unless they adopt a refinement procedure with a high computational complexity. How the existing transmission estimation methods suffer from the halo artefact at the boundaries are analysed and it is found that the elaborate, high computational refinement procedure to remove the halo effect are excessive for dehazing. On the basis of the above-mentioned analysis and finding, a simple segmentation logic was embedded in an existing transmission estimator, which is sufficiently accurate for dehazing. The experiment verifies that the proposed method significantly reduces the halo artefact without requiring any refinement procedure. [ABSTRACT FROM AUTHOR], Copyright of Electronics Letters is the property of Institution of Engineering {\{}{\&}{\}} Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. Howe)},
author = {Bui, T.M. and Tran, H.N. and Kim, W. and Kim, S.},
doi = {10.1049/el.2013.3652},
file = {::},
issn = {0013-5194},
journal = {Electronics Letters},
number = {7},
pages = {516--518},
title = {{Segmenting dark channel prior in single image dehazing}},
url = {http://digital-library.theiet.org/content/journals/10.1049/el.2013.3652},
volume = {50},
year = {2014}
}
@article{Shamonin2013,
abstract = {Nonrigid image registration is an important, but time-consuming taskin medical image analysis. In typical neuroimaging studies, multipleimage registrations are performed, i.e. for atlas-based segmentationor template construction. Faster image registration routines wouldtherefore be beneficial.In this paper we explore acceleration of the image registrationpackage elastix by a combination of several techniques: i)parallelization on the CPU, to speed up the cost function derivativecalculation; ii) parallelization on the GPU building on andextending the OpenCL framework from ITKv4, to speed up the Gaussianpyramid computation and the image resampling step; iii) exploitationof certain properties of the B-spline transformation model; iv)further software optimizations.The accelerated registration tool is employed in a study ondiagnostic classification of Alzheimer's disease and cognitivelynormal controls based on T1-weighted MRI. We selected 299participants from the publicly available Alzheimer's DiseaseNeuroimaging Initiative database. Classification is performed with asupport vector machine based on gray matter volumes as a marker foratrophy. We evaluated two types of strategies (voxel-wise andregion-wise) that heavily rely on nonrigid image registration.Parallelization and optimization resulted in an acceleration factorof 4-5x on an 8-core machine. Using OpenCL a speedup factor of {\~{}}2was realized for computation of the Gaussian pyramids, and 15-60 forthe resampling step, for larger images. The voxel-wise and theregion-wise classification methods had an area under thereceiver operator characteristic curve of 88{\%} and 90{\%},respectively, both for standard and accelerated registration.We conclude that the image registration package elastix wassubstantially accelerated, with nearly identical results to thenon-optimized version. The new functionality will become availablein the next release of elastix as open source under the BSD license.},
author = {Shamonin, Denis and Bron, Esther E and Lelieveldt, Boudewijn P.F. and Smits, Marion and Klein, Stefan and Staring, Marius},
doi = {10.3389/fninf.2013.00050},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shamonin et al. - 2013 - Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease.pdf:pdf},
issn = {16625196},
journal = {Frontiers in Neuroinformatics},
keywords = {Acceleration,Alzheimer's disease,Elastix,OpenCL,image registration,parallelization},
pages = {50},
publisher = {Frontiers},
title = {{Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease}},
url = {http://journal.frontiersin.org/article/10.3389/fninf.2013.00050/abstract},
volume = {7},
year = {2013}
}
@article{roerdink2000watershed,
author = {Roerdink, J.B.T.M. and Meijster, A},
journal = {Fundamenta Informaticae},
number = {1-2},
pages = {187--228},
title = {{The watershed transform: Definitions, algorithms and parallelization strategies}},
volume = {41},
year = {2000}
}
@article{Chai2014,
abstract = {Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.},
author = {Chai, T. and Draxler, R. R.},
doi = {10.5194/gmd-7-1247-2014},
isbn = {1991-9603},
issn = {19919603},
journal = {Geoscientific Model Development},
number = {3},
pages = {1247--1250},
title = {{Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature}},
volume = {7},
year = {2014}
}
@misc{,
booktitle = {https://www.raspberrypi.org/},
title = {{No Title}},
url = {https://www.raspberrypi.org/}
}
@article{Sun2009,
abstract = {Lithium difluoro(axalato)borate (LiODFB) was synthesized in di-Me carbonate (DMC) solvent and purified by the method of solventing-out crystn. The structure characterization of the purified LiODFB was performed by Fourier transform IR (FTIR) spectrometry and NMR (NMR) spectrometry. The electrochem. properties of the cells using 1 mol/L LiPF6 and 1 mol/L LiODFB in ethylene carbonate (EC)/DMC were investigated, resp. The results indicate that LiODFB can be reduced at about 1.5 V and form a robust protective solid electrolyte interface (SEI) film on the graphite surface in the first cycle. The graphite/LiNi1/3Mn1/3Co1/3O2 cells with LiODFB-based electrolyte have very good capacity retention at 55 DegC, and show very good rate capability at 0.5C and 1C charge/discharge rate. Therefore, as a new salt, LiODFB is a most promising alternative lithium salt to replace LiPF6 for lithium ion battery electrolytes in the future. [on SciFinder (R)]},
author = {Sun, Lushi and Zhao, Qingsen and Xiang, Jun and Shi, Jinming and Wang, Lele and Hu, Song and Su, Sheng},
doi = {10.1007/s11771},
file = {::},
isbn = {0400504103},
issn = {04381157},
journal = {Huagong Xuebao/CIESC Journal},
keywords = {Adsorption,CuO/$\gamma$-Al2O3,Diffuse reflectance infrared fourier transform spe,NH3,NO,Selective catalytic reduction},
number = {2},
pages = {444--449},
title = {{Adsorption of NO and NH3 over CuO/$\gamma$-Al2O3 catalyst by DRIFTS}},
volume = {60},
year = {2009}
}
@article{,
file = {::},
isbn = {9781467325332},
journal = {Ieee},
number = {2},
pages = {969--972},
title = {{TEMPORALLY COHERENT REAL-TIME VIDEO DEHAZING Intelligent Video Tech . Lab ., SK Telecom , Korea School of ECE , Ulsan National Institute of Science and Technology , Korea}},
year = {2012}
}
@article{1543504,
author = {Valet, L and Mauris, G and Bolon, P},
doi = {10.1109/62.911315},
journal = {IEEE Aero El Sys Mag},
number = {3},
pages = {7--14},
title = {{A statistical overview of recent literature in information fusion}},
volume = {16},
year = {2001}
}
@inproceedings{schechner2001instant,
address = {Kauai, USA},
author = {Schechner, Yoav Y and Narasimhan, Srinivasa G and Nayar, Shree K},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)},
file = {::},
month = {dec},
pages = {I----325},
title = {{Instant dehazing of images using polarization}},
volume = {16},
year = {2001}
}
@inproceedings{russell2006using,
author = {Russell, B C and Freeman, W T and Efros, A A and Sivic, J and Zisserman, A},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)},
doi = {10.1109/CVPR.2006.326},
issn = {1063-6919},
keywords = {Artificial intelligence;Computer science;Concrete;},
month = {jun},
pages = {1605--1614},
title = {{Using Multiple Segmentations to Discover Objects and their Extent in Image Collections}},
volume = {2},
year = {2006}
}
@article{Lai2012,
author = {Lai, Yi-shuan and Chen, Yi-lei and Hsu, Chiou-ting},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lai, Chen, Hsu - 2012 - Single Image Dehazing With Optimal Transmission Map.pdf:pdf},
isbn = {9784990644109},
issn = {10514651},
journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
keywords = {Enhancement,Restoration and Filtering,Scene Understanding},
number = {Icpr},
pages = {388--391},
title = {{Single Image Dehazing With Optimal Transmission Map}},
volume = {25},
year = {2012}
}
@inproceedings{6909779,
address = {Columbus, USA},
author = {Tang, Ketan and Yang, Jianchao and Wang, Jue},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
doi = {10.1109/CVPR.2014.383},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Tang, Yang, Wang - 2014 - Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing.pdf:pdf},
keywords = {feature extraction;image denoising;learning (artif},
month = {jun},
pages = {2995--3002},
title = {{Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing}},
volume = {1},
year = {2014}
}
@inproceedings{tan2008visibility,
address = {Anchorage, USA},
author = {Tan, R T},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
file = {::},
issn = {1063-6919},
keywords = {Markov processes;computer vision;graph theory;imag},
month = {jun},
pages = {1--8},
title = {{Visibility in bad weather from a single image}},
volume = {1},
year = {2008}
}
@inproceedings{6566464,
address = {Melbourne, Australia},
author = {He, Renjie and Wang, Zhiyong and Fan, Yangyu and Feng, D D},
booktitle = {IEEE Conference on Industrial Electronics and Applications (ICIEA)},
doi = {10.1109/ICIEA.2013.6566464},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/He et al. - 2013 - Multiple scattering model based single image dehazing.pdf:pdf},
keywords = {image reconstruction;atmospheric point spread func},
month = {jun},
pages = {733--737},
title = {{Multiple scattering model based single image dehazing}},
volume = {1},
year = {2013}
}
@inproceedings{5975838,
address = {Beijing, China},
author = {Lin, Che-Chung and Lin, Yu-Chen and Chen, Long-Tai and Fu, Yu-Shiang and Lin, Weng-Ching},
booktitle = {IEEE Conference on Industrial Electronics and Applications (ICIEA)},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lin et al. - 2011 - A novel fast dehazing algorithm in driver assistance system.pdf:pdf},
month = {jun},
pages = {1560--1563},
title = {{A novel fast dehazing algorithm in driver assistance system}},
volume = {1},
year = {2011}
}
@article{Long2014,
abstract = {Remote sensing images are widely used in various fields. However, they usually suffer from the poor contrast caused by haze. In this letter, we propose a simple, but effective, way to eliminate the haze effect on remote sensing images. Our work is based on the dark channel prior and a common haze imaging model. In order to eliminate halo artifacts, we use a low-pass Gaussian filter to refine the coarse estimated atmospheric veil. We then redefine the transmission, with the aim of preventing the color distortion of the recovered images. The main advantage of the proposed algorithm is its fast speed, while it can also achieve good results. The experimental results demonstrate that our algorithm produces visually appealing dehazing images and retains the very fine details. Moreover, for images containing partly clear and partly hazy areas, our algorithm can also achieve good results.},
author = {Long, Jiao and Shi, Zhenwei and Tang, Wei and Zhang, Changshui},
doi = {10.1109/LGRS.2013.2245857},
isbn = {1545-598X VO  - 11},
issn = {1545598X},
journal = {IEEE Geoscience and Remote Sensing Letters},
keywords = {Dark channel,Gaussian filter,image dehazing,remote sensing},
number = {1},
pages = {59--63},
title = {{Single remote sensing image dehazing}},
volume = {11},
year = {2014}
}
@inproceedings{7178242,
address = {South Brisbane, Australia},
author = {Li, Zhengguo and Zheng, Jinghong and Yao, Wei and Zhu, Zijian},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
doi = {10.1109/ICASSP.2015.7178242},
file = {::},
isbn = {9781467369978},
issn = {15206149},
keywords = {Atmosphere,Channel estimation,Colored noise,Comput},
month = {apr},
number = {1},
pages = {1608--1612},
title = {{Single image haze removal via a simplified dark channel}},
volume = {1},
year = {2015}
}
@inproceedings{kim2011single,
abstract = {A simple and adaptive single image dehazing algorithm is proposed in this work. Based on the observation that a hazy image has low contrast in general, we attempt to restore the original image by enhancing the contrast. First, the proposed algorithm estimates the airlight in a given hazy image based on the quad-tree subdivision. Then, the proposed algorithm estimates the transmission map to maximize the contrast of the output image. To measure the contrast, we develop a cost function, which consists of a standard deviation term and a histogram uniformness term. Experimental results demonstrate that the proposed algorithm can remove haze efficiently and reconstruct fine details in original scenes clearly.},
address = {Prague, Czech},
author = {Kim, Jin-Hwan Hwan and Sim, Jae-Young Young and Kim, Chang-Su Su},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
doi = {10.1109/ICASSP.2011.5946643},
file = {::},
isbn = {9781457705397},
issn = {15206149},
keywords = {Image enhancement,contrast enhancement,image restoration,single image dehazing},
month = {may},
pages = {1273--1276},
title = {{Single image dehazing based on contrast enhancement}},
volume = {1},
year = {2011}
}
@inproceedings{5585096,
address = {Cambridge, England},
author = {Joshi, N and Cohen, M F},
booktitle = {IEEE International Conference on Computational Photography (ICCP)},
doi = {10.1109/ICCPHOT.2010.5585096},
keywords = {image denoising;image enhancement;photography;quan},
month = {mar},
pages = {1--8},
title = {{Seeing Mt. Rainier: Lucky imaging for multi-image denoising, sharpening, and haze removal}},
volume = {1},
year = {2010}
}
@inproceedings{sulami2014,
address = {Santa Clara, USA},
author = {Sulami, M and Glatzer, I and Fattal, R and Werman, M},
booktitle = {IEEE International Conference on Computational Photography (ICCP)},
doi = {10.1109/ICCPHOT.2014.6831817},
file = {::},
keywords = {atmospheric light propagation;brightness;error sta},
month = {may},
pages = {1--11},
title = {{Automatic recovery of the atmospheric light in hazy images}},
volume = {1},
year = {2014}
}
@inproceedings{jin2012single,
address = {Zhangjiajie, China},
author = {Jin, Wenbo and Mi, Zengyuan and Wu, Xiaotian and Huang, Yue and Ding, Xinghao},
booktitle = {IEEE International Conference on Computer Science and Automation Engineering (CSAE)},
doi = {10.1109/CSAE.2012.6272884},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Jin et al. - 2012 - Single image de-haze based on a new dark channel estimation method.pdf:pdf},
keywords = {bilateral filter;dark channel prior;de-haze;fog;im},
month = {may},
pages = {791--795},
title = {{Single image de-haze based on a new dark channel estimation method}},
volume = {2},
year = {2012}
}
@inproceedings{6442455,
address = {Kyoto, Japan},
author = {Tarel, J.-P. and Hautiere, N},
booktitle = {IEEE International Conference on Computer Vision (ICCV)},
file = {::},
issn = {1550-5499},
keywords = {Atmospheric modeling;Cameras;Color;Image restorati},
month = {sep},
pages = {2201--2208},
title = {{Fast visibility restoration from a single color or gray level image}},
volume = {1},
year = {2009}
}
@inproceedings{martin2001database,
author = {Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
booktitle = {IEEE International Conference on Computer Vision (ICCV)},
pages = {416--423},
title = {{A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics}},
volume = {2},
year = {2001}
}
@inproceedings{5651835,
abstract = {applicability for this approach.},
address = {Kyoto, Japan},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Kratz, Louis and Nishino, Ko},
booktitle = {IEEE International Conference on Computer Vision (ICCV)},
doi = {10.1109/ICCV.2009.5459382},
eprint = {arXiv:1011.1669v3},
file = {::},
isbn = {9781424444205},
issn = {1550-5499},
keywords = {Markov processes,expectation-maximisation algorith},
month = {sep},
number = {Iccv},
pages = {1701--1708},
pmid = {25246403},
title = {{Factorizing Scene Albedo and Depth from a Single Foggy Image}},
volume = {1},
year = {2009}
}
@inproceedings{7066446,
address = {Las Vegas, USA},
author = {Park, Ki Tae and Kim, Yong Min and Moon, Young Shik},
booktitle = {IEEE International Conference on Consumer Electronics (ICCE)},
doi = {10.1109/ICCE.2015.7066446},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Park, Kim, Moon - 2015 - An efficient dehazing method for edge enhancement by using entropy-map.pdf:pdf},
keywords = {edge detection;filtering theory;image colour analy},
month = {jan},
pages = {362--363},
title = {{An efficient dehazing method for edge enhancement by using entropy-map}},
volume = {1},
year = {2015}
}
@inproceedings{5651964,
address = {Hong Kong, China},
author = {Fang, Shuai and Zhan, Jiqing and Cao, Yang and Rao, Ruizhong},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2010.5651964},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Fang et al. - 2010 - Improved single image dehazing using segmentation.pdf:pdf},
issn = {1522-4880},
keywords = {image enhancement;image restoration;image segmenta},
month = {sep},
pages = {3589--3592},
title = {{Improved single image dehazing using segmentation}},
volume = {1},
year = {2010}
}
@inproceedings{6467023,
address = {Orlando, USA},
author = {Kim, Jin-Hwan and Jang, Won-Dong and Park, Yongsup and Lee, Dong-Hahk and Sim, Jae-Young and Kim, Chang-Su},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2012.6467023},
issn = {1522-4880},
keywords = {computational complexity;edge detection;filtering},
month = {sep},
pages = {969--972},
title = {{Temporally x real-time video dehazing}},
volume = {1},
year = {2012}
}
@inproceedings{5651263,
abstract = {This papers introduces a new single image dehazing approach. The method employs a fusion-based strategy that takes as inputs two adapted versions of the original image that are weighted by specific maps in order to yield accurate haze-free results. The method computes in a per-pixel fashion being straightforward to be implemented. Our effective method demonstrates to yield comparative and even better results than the more complex state-of-the-art techniques but has the advantage to be appropriate for real-time applications.},
address = {Hong Kong, China},
author = {Ancuti, Codruta Orniana Cosmin O and Ancuti, Codruta Orniana Cosmin O and Bekaert, Philippe},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2010.5651263},
file = {::},
isbn = {9781424479948},
issn = {1522-4880},
keywords = {f,image fusion,image restoration,real-time systems},
month = {sep},
pages = {3541--3544},
title = {{Effective single image dehazing by fusion}},
volume = {1},
year = {2010}
}
@inproceedings{7025924,
address = {Paris, France},
author = {Zhang, Jing and Cao, Yang and Wang, Zengfu},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
month = {oct},
pages = {4557--4561},
title = {{Nighttime haze removal based on a new imaging model}},
volume = {1},
year = {2014}
}
@inproceedings{schaul2009color,
abstract = {In landscape photography, distant objects often appear blurred with a blue color cast, a degradation caused by atmospheric haze. To enhance image contrast, pleasantness and information content, dehazing can be performed. We propose that fusing a visible and an near-infrared (NIR) image of the same scene results in a dehazed color image without the need for haze or airlight detection or the generation of depth maps. This is achieved through a multiresolution approach using edge-preserving filtering to minimize artifacts. The near-infrared part of the spectrum is easy to acquire with normal digital cameras. The NIR images are generally devoid of haze as it is an inherent function of the wavelengths. Experiments on real images validate our approach.},
address = {Cairo, Egypt},
author = {Schaul, Lex and Fredembach, Cl{\'{e}}ment and Susstrunk, S and S{\"{u}}sstrunk, Sabine},
booktitle = {IEEE International Conference on Image Processing (ICIP)},
doi = {10.1109/ICIP.2009.5413700},
file = {::},
isbn = {9781424456543},
issn = {15224880},
keywords = {Edge-preserving filters,Haze,Image fusion,Multi-resolution,Near-infrared,Scattering,colour photography,image colour analysis,image enh},
month = {nov},
pages = {1629--1632},
title = {{Color image dehazing using the near-infrared}},
volume = {1},
year = {2009}
}
@inproceedings{7090317,
address = {Bali, indonesia},
author = {Mai, Jiaming and Zhu, Qingsong and Wu, Di},
booktitle = {IEEE International Conference on Robotics and Biomimetics (ROBIO)},
doi = {10.1109/ROBIO.2014.7090317},
file = {::},
keywords = {ambiguity,image colour analysis,image restoration},
month = {dec},
pages = {118--123},
title = {{The latest challenges and opportunities in the current single image dehazing algorithms}},
volume = {1},
year = {2014}
}
@inproceedings{6986190,
address = {Guilin, China},
author = {Lu, Xipan and Lv, Guoyun and Lei, Tao},
booktitle = {IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lu, Lv, Lei - 2014 - Single image dehazing based on multiple scattering model.pdf:pdf},
month = {aug},
pages = {239--244},
title = {{Single image dehazing based on multiple scattering model}},
volume = {1},
year = {2014}
}
@inproceedings{6974568,
address = {San Diego, USA},
author = {Zhu, Qingsong and Mai, Jiaming and Song, Zhan and Wu, Di and Wang, Jianjun and Wang, Lei},
booktitle = {IEEE International Conference on Systems, Man and Cybernetics (SMC)},
doi = {10.1109/SMC.2014.6974568},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Zhu et al. - 2014 - Mean shift-based single image dehazing with re-refined transmission map.pdf:pdf},
keywords = {image denoising;image enhancement;image restoratio},
month = {oct},
pages = {4058--4064},
title = {{Mean shift-based single image dehazing with re-refined transmission map}},
volume = {1},
year = {2014}
}
@article{cox1977optics,
author = {Cantor, A},
doi = {10.1109/JQE.1978.1069864},
issn = {0018-9197},
journal = {IEEE Journal of Quantum Electronics (JQE)},
keywords = {Books;Equations;Meteorology;Mie scattering;Optical},
number = {9},
pages = {698--699},
title = {{Optics of the atmosphere--Scattering by molecules and particles}},
volume = {14},
year = {1978}
}
@article{Bilotta2013,
author = {Bilotta, Giuseppe and Sanchez, Ricardo Zanmar and Ganci, Gaetana},
doi = {10.1109/JSTARS.2013.2255261},
issn = {1939-1404},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
month = {dec},
number = {6},
pages = {2445--2452},
title = {{Optimizing Satellite Monitoring of Volcanic Areas Through GPUs and Multi-Core CPUs Image Processing: An OpenCL Case Study}},
url = {http://ieeexplore.ieee.org/document/6502751/},
volume = {6},
year = {2013}
}
@article{8071244,
abstract = {Light scattering produced for bad weather conditions produces outdoor images with poor contrast and faded colors, these effects can be critical in applications such as video surveillance, driving assistance or autonomous navigation. This article introduces a novel algorithm to restore the contrast of images under adverse weather conditions e.g.; fog, mist or haze. The proposed method combines several techniques in order to provide a fast algorithm able to work with color as well as gray images. Although other deweathering methods require multiple images of the scene or information about the weather conditions or the scene structure the proposed method only requires a single image of the foggy scene and assumes heterogeneous atmospheric conditions which is a common feature in images affected for weather conditions. Experiment results on real-world datasets validate the effectiveness of the proposed method.},
author = {Andrade, J},
doi = {10.1109/TLA.2017.8071244},
issn = {1548-0992},
journal = {IEEE Latin America Transactions},
keywords = {atmospheric techniques;fog;geophysical image proce},
month = {oct},
number = {10},
pages = {1983--1987},
title = {{Improvement of visibility under foggy conditions}},
volume = {15},
year = {2017}
}
@article{1464600,
author = {Wald, Lucien},
doi = {10.1109/36.763269},
journal = {IEEE Trans Geosci Remote Sensing},
number = {3},
pages = {1190--1193},
title = {{Some terms of reference in data fusion}},
volume = {37},
year = {1999}
}
@article{Kanungo,
address = {Washington, DC, USA},
author = {Kanungo, Tapas and Mount, David M and Netanyahu, Nathan S and Piatko, Christine D and Silverman, Ruth and Wu, Angela Y},
doi = {10.1109/TPAMI.2002.1017616},
issn = {0162-8828},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
keywords = {Pattern recognition,computational geometry,data mining,k-d tree,k-means clustering,knowledge discovery.,machine learning,nearest-neighbor searching},
month = {jul},
number = {7},
pages = {881--892},
publisher = {IEEE Computer Society},
title = {{An Efficient k-Means Clustering Algorithm: Analysis and Implementation}},
url = {http://dx.doi.org/10.1109/TPAMI.2002.1017616},
volume = {24},
year = {2002}
}
@article{Ko1991,
abstract = {The center weighted median (CWM) filter, which is a weighted median filter giving more weight only to the central value of each window, is studied. This filter can preserve image details while suppressing additive white and/or impulsive-type noise. The statistical properties of the CWM filter are analyzed. It is shown that the CWM filter can outperform the median filter. Some relationships between CWM and other median-type filters, such as the Winsorizing smoother and the multistage median filter, are derived. In an attempt to improve the performance of CWM filters, an adaptive CWM (ACWM) filter having a space varying central weight is proposed. We show that the ACWM filter is an excel- lent detail preserving smoother that can suppress signal-depen- dent noise as well as signal-independent noise. I.},
author = {Ko, Sung Jea and Lee, Yong Hoon},
doi = {10.1109/31.83870},
file = {::},
isbn = {0098-4094},
issn = {00984094},
journal = {IEEE Transactions on Circuits and Systems},
number = {9},
pages = {984--993},
title = {{Center Weighted Median Filters and Their Applications to Image Enhancement}},
volume = {38},
year = {1991}
}
@article{Shiau2013,
author = {Shiau, Yeu-Horng and Yang, Hung-Yu and Chen, Pei-Yin and Chuang, Ya-Zhu},
doi = {10.1109/TCSVT.2013.2243650},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shiau et al. - 2013 - Hardware Implementation of a Fast and Efficient Haze Removal Method.pdf:pdf},
issn = {1051-8215},
journal = {IEEE Transactions on Circuits and Systems for Video Technology (CSVT)},
keywords = {filtering theory;geophysical image processing;pipe},
month = {aug},
number = {8},
pages = {1369--1374},
title = {{Hardware Implementation of a Fast and Efficient Haze Removal Method}},
volume = {23},
year = {2013}
}
@article{6827208,
author = {Lai, Yi-Hsuan and Chen, Yi-Lei and Chiou, Chuan-Ju and Hsu, Chiou-Ting},
doi = {10.1109/TCSVT.2014.2329381},
file = {::},
issn = {1051-8215},
journal = {IEEE Transactions on Circuits and Systems for Video Technology (CSVT)},
keywords = {image colour analysis;minimisation;quadratic progr},
month = {jan},
number = {1},
pages = {1--14},
title = {{Single-Image Dehazing via Optimal Transmission Map Under Scene Priors}},
volume = {25},
year = {2015}
}
@article{hazedetectionand,
author = {Du, Yong and Guindon, B and Cihlar, J},
doi = {10.1109/36.981363},
file = {::},
issn = {0196-2892},
journal = {IEEE Transactions on Geoscience and Remote Sensing (TGRS)},
keywords = {atmospheric optics;geophysical signal processing;g},
number = {1},
pages = {210--217},
title = {{Haze detection and removal in high resolution satellite image with wavelet analysis}},
volume = {40},
year = {2002}
}
@article{quantitativeassesmentof,
author = {Zhang, Ying and Guindon, Bert},
file = {::},
journal = {IEEE Transactions on Geoscience and Remote Sensing (TGRS)},
number = {5},
pages = {1082--1089},
publisher = {IEEE},
title = {{Quantitative assessment of a haze suppression methodology for satellite imagery: effect on land cover classification performance}},
volume = {41},
year = {2003}
}
@article{Oakley1998,
abstract = {In daylight viewing conditions, image contrast is often significantly degraded by atmospheric aerosols such as haze and fog. This paper introduces a method for reducing this degradation in situations in which the scene geometry is known. Contrast is lost because light is scattered toward the sensor by the aerosol particles and because the light reflected by the terrain is attenuated by the aerosol. This degradation is approximately characterized by a simple, physically based model with three parameters. The method involves two steps: first, an inverse problem is solved in order to recover the three model parameters; then, for each pixel, the relative contributions of scattered and reflected flux are estimated. The estimated scatter contribution is simply subtracted from the pixel value and the remainder is scaled to compensate for aerosol attenuation. This paper describes the image processing algorithm and presents an analysis of the signal-to-noise ratio (SNR) in the resulting enhanced image. This analysis shows that the SNR decreases exponentially with range. A temporal filter structure is proposed to solve this problem. Results are presented for two image sequences taken from an airborne camera in hazy conditions and one sequence in clear conditions. A satisfactory agreement between the model and the experimental data is shown for the haze conditions. A significant improvement in image quality is demonstrated when using the contrast enhancement algorithm in conjuction with a temporal filter.},
author = {Oakley, John P. and Satherley, Brenda L.},
doi = {10.1109/83.660994},
file = {::},
isbn = {1057-7149},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
number = {2},
pages = {167--179},
pmid = {18267391},
title = {{Improving image quality in poor visibility conditions using a physical model for contrast degradation}},
volume = {7},
year = {1998}
}
@article{Vincent1993a,
author = {Vincent, L},
file = {::},
journal = {IEEE Transactions on Image Processing},
number = {2},
pages = {176--201},
title = {{Morphological Grayscale Reconstruction in Image Analysis}},
volume = {2},
year = {1993}
}
@article{Zhang2011,
abstract = {Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.},
author = {Zhang, Lin and Zhang, Lei and Mou, Xuanqin and Zhang, David},
doi = {10.1109/TIP.2011.2109730},
isbn = {1057-7149},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {Gradient,image quality assessment (IQA),low-level feature,phase congruency (PC)},
number = {8},
pages = {2378--2386},
pmid = {21292594},
title = {{FSIM: A feature similarity index for image quality assessment}},
volume = {20},
year = {2011}
}
@article{7128396,
abstract = {Single image haze removal has been a challenging problem due to its ill-posed nature. In this paper, we propose a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the sceneradiancevia theatmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect},
author = {Zhu, Qingsong and Mai, Jiaming and Shao, Ling},
doi = {10.1109/TIP.2015.2446191},
file = {::;::},
issn = {1057-7149},
journal = {IEEE Transactions on Image Processing},
keywords = {Dehazing,defog,depth restoration,image colour analysis,image restoration,learning (},
month = {nov},
number = {11},
pages = {3522--3533},
pmid = {26099141},
title = {{A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior}},
volume = {24},
year = {2015}
}
@article{2001032,
author = {Haralick, R M and Sternberg, Stanley R and Zhuang, Xinhua},
doi = {10.1109/TPAMI.1987.4767941},
issn = {0162-8828},
journal = {IEEE Transactions on Image Processing (TIP)},
keywords = {Computer architecture;Convolution;Image analysis;I},
number = {4},
pages = {532--550},
title = {{Image Analysis Using Mathematical Morphology}},
volume = {9},
year = {1987}
}
@article{1284395,
author = {Wang, Zhou and Bovik, A C and Sheikh, H R and Simoncelli, E P},
doi = {10.1109/TIP.2003.819861},
issn = {1057-7149},
journal = {IEEE Transactions on Image Processing (TIP)},
keywords = {Automated;Quality Control;Reproducibility of Resu,Computer-Assisted;Information Storage and Retriev,Computer-Assisted;Subtraction Technique,Statistical;Hypermedia;Image Enhancement;Image In,Statistical;Pattern Recognition,data compression;image coding;visual perception;JP},
number = {4},
pages = {600--612},
title = {{Image quality assessment: from error visibility to structural similarity}},
volume = {13},
year = {2004}
}
@article{bb28643,
author = {Cardoso, J S and Corte-Real, L},
journal = {IEEE Transactions on Image Processing (TIP)},
number = {11},
pages = {1773--1782},
publisher = {IEEE},
title = {{Toward a generic evaluation of image segmentation}},
volume = {14},
year = {2005}
}
@article{Gibson2012a,
abstract = {This paper makes an investigation of the dehazing effects on image and video coding for surveillance systems. The goal is to achieve good dehazed images and videos at the receiver while sustaining low bitrates (using compression) in the transmission pipeline. At first, this paper proposes a novel method for single-image dehazing, which is used for the investigation. It operates at a faster speed than current methods and can avoid halo effects by using the median operation. We then consider the dehazing effects in compression by investigating the coding artifacts and motion estimation in cases of applying any dehazing method before or after compression. We conclude that better dehazing performance with fewer artifacts and better coding efficiency is achieved when the dehazing is applied before compression. Simulations for Joint Photographers Expert Group images in addition to subjective and objective tests with H.264 compressed sequences validate our conclusion.},
author = {Gibson, Kristofor B and V{\~{o}}, Dung T and Nguyen, Truong Q},
doi = {10.1109/TIP.2011.2166968},
isbn = {1057-7149},
issn = {1941-0042},
journal = {IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
number = {2},
pages = {662--73},
pmid = {21896391},
title = {{An investigation of dehazing effects on image and video coding.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21896391},
volume = {21},
year = {2012}
}
@article{6175945,
author = {Bayram, S and Sencar, H T and Memon, N},
doi = {10.1109/TIFS.2012.2192272},
issn = {1556-6013},
journal = {IEEE Transactions on Information Forensics and Security},
keywords = {error statistics;fingerprint identification;image},
month = {aug},
number = {4},
pages = {1404--1413},
title = {{Efficient Sensor Fingerprint Matching Through Fingerprint Binarization}},
volume = {7},
year = {2012}
}
@article{sabuncu2010generative,
author = {Sabuncu, M R and Yeo, B T T and {Van Leemput}, K and Fischl, B and Golland, P},
journal = {IEEE Transactions on Medical Imaging (T-MI)},
number = {10},
pages = {1714--1729},
publisher = {IEEE},
title = {{A generative model for image segmentation based on label fusion}},
volume = {29},
year = {2010}
}
@article{He2013,
abstract = {In this paper, we propose a novel explicit image filter called guided filter. Derived from a local linear model, the guided filter computes the filtering output by considering the content of a guidance image, which can be the input image itself or another different image. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter [1], but it has better behaviors near edges. The guided filter is also a more generic concept beyond smoothing: It can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and nonapproximate linear time algorithm, regardless of the kernel size and the intensity range. Currently, it is one of the fastest edge-preserving filters. Experiments show that the guided filter is both effective and efficient in a great variety of computer vision and computer graphics applications, including edge-aware smoothing, detail enhancement, HDR compression, image matting/feathering, dehazing, joint upsampling, etc.},
author = {He, Kaiming and Sun, Jian and Tang, Xiaoou},
doi = {10.1109/TPAMI.2012.213},
file = {::},
isbn = {3642155480},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Bilateral filter,Edge-preserving filtering,Linear time filtering},
number = {6},
pages = {1397--1409},
pmid = {23599054},
title = {{Guided image filtering}},
volume = {35},
year = {2013}
}
@article{Levin2008,
abstract = {Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed -- at each pixel we must estimate the foreground and the background colors, as well as the foreground opacity ("alpha matte") from a single color measurement. Current approaches either restrict the estimation to a small part of the image, estimating foreground and background colors based on nearby pixels where they are known, or perform iterative nonlinear estimation by alternating foreground and background color estimation with alpha estimation. In this paper we present a closed-form solution to natural image matting. We derive a cost function from local smoothness assumptions on foreground and background colors, and show that in the resulting expression it is possible to analytically eliminate the foreground and background colors to obtain a quadratic cost function in alpha. This allows us to find the globally optimal alpha matte by solving a sparse linear system of equations. Furthermore, the closed-form formula allows us to predict the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms. We show that high quality mattes for natural images may be obtained from a small amount of user input.},
author = {Levin, Anat and Lischinski, Dani and Weiss, Yair},
doi = {10.1109/TPAMI.2007.1177},
isbn = {0162-8828},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Interactive image editing,Matting,Spectral segmentation},
number = {2},
pages = {228--242},
pmid = {18084055},
title = {{A closed-form solution to natural image matting}},
volume = {30},
year = {2008}
}
@article{Lee1980,
abstract = {Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.},
author = {Lee, Jong See},
doi = {10.1109/TPAMI.1980.4766994},
file = {::},
isbn = {0162-8828 VO - PAMI-2},
issn = {0162-8828},
journal = {IEEE transactions on pattern analysis and machine intelligence},
number = {2},
pages = {165--168},
pmid = {21868887},
title = {{Digital image enhancement and noise filtering by use of local statistics.}},
volume = {2},
year = {1980}
}
@misc{Nitzberg1992,
abstract = {The authors describe a model of nonlinear image filtering for noise reduction and edge enhancement using anisotropic diffusion. The method is designed to enhance not only edges, but corners and T junctions as well. The method roughly corresponds to a nonlinear diffusion process with backward heat flow across the strong edges. Such a process is ill posed, making the results depend strongly on how the algorithm differs from the diffusion process. Two ways of modifying the equations using simulations on a variable grid are studied},
author = {Nitzberg, Mark and Shiota, Takahiro},
booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
doi = {10.1109/34.149593},
file = {::},
issn = {01628828},
keywords = {Adaptive filtering,T junc-tion,corner detection,corner enhance-,edge enhancement,ment,nonlinear diffusion,nonlinear filtering},
number = {8},
pages = {826--833},
title = {{Nonlinear Image Filtering with Edge and Corner Enhancement}},
volume = {14},
year = {1992}
}
@article{5567108,
author = {He, Kaiming and Sun, Jian and Tang, Xiaoou},
doi = {10.1109/TPAMI.2010.168},
file = {::},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {color c,image colour analysis,statistical analysis},
number = {12},
pages = {2341--2353},
title = {{Single Image Haze Removal Using Dark Channel Prior}},
volume = {33},
year = {2010}
}
@article{Phung2005,
abstract = {This paper presents a study of three important issues of the color pixel classification approach to skin segmentation: color representation, color quantization, and classification algorithm. Our analysis of several representative color spaces using the Bayesian classifier with the histogram technique shows that skin segmentation based on color pixel classification is largely unaffected by the choice of the color space. However, segmentation performance degrades when only chrominance channels are used in classification. Furthermore, we find that color quantization can be as low as 64 bins per channel, although higher histogram sizes give better segmentation performance. The Bayesian classifier with the histogram technique and the multilayer perceptron classifier are found to perform better compared to other tested classifiers, including three piecewise linear classifiers, three unimodal Gaussian classifiers, and a Gaussian mixture classifier.},
author = {Phung, Son Lam and Bouzerdoum, Abdesselam and Chai, Douglas},
doi = {10.1109/TPAMI.2005.17},
isbn = {0162-8828},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Classifier design and evaluation,Color space,Face detection,Pixel classification,Skin segmentation},
number = {1},
pages = {148--154},
pmid = {15628277},
title = {{Skin segmentation using color pixel classification: Analysis and comparison}},
volume = {27},
year = {2005}
}
@article{unnikrishnan2007toward,
author = {Unnikrishnan, Ranjith and Pantofaru, Caroline and Hebert, Martial},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
number = {6},
pages = {929--944},
publisher = {IEEE},
title = {{Toward objective evaluation of image segmentation algorithms}},
volume = {29},
year = {2007}
}
@article{comaniciu2002mean,
author = {Comaniciu, D and Meer, P},
doi = {10.1109/34.1000236},
issn = {0162-8828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
month = {may},
number = {5},
pages = {603--619},
title = {{Mean shift: a robust approach toward feature space analysis}},
volume = {24},
year = {2002}
}
@article{Zhengfa,
author = {Liang, Zhengfa and Liu, Hengzhu and Zhang, Botao and Wang, Benzhang},
doi = {10.1587/elex.11.20141002},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Liang et al. - 2014 - Real-time Hardware Accelerator for Single Image Haze Removal Using Dark Channel Prior and Guided Filter.pdf:pdf},
journal = {IEICE Electronics Express},
number = {1},
pages = {1--11},
title = {{Real-time Hardware Accelerator for Single Image Haze Removal Using Dark Channel Prior and Guided Filter}},
volume = {1},
year = {2014}
}
@article{Fausett2014,
abstract = {Mycotoxins are small (MW approximately 700), toxic chemical products formed as secondary metabolites by a few fungal species that readily colonise crops and contaminate them with toxins in the field or after harvest. Ochratoxins and Aflatoxins are mycotoxins of major significance and hence there has been significant research on broad range of analytical and detection techniques that could be useful and practical. Due to the variety of structures of these toxins, it is impossible to use one standard technique for analysis and/or detection. Practical requirements for high-sensitivity analysis and the need for a specialist laboratory setting create challenges for routine analysis. Several existing analytical techniques, which offer flexible and broad-based methods of analysis and in some cases detection, have been discussed in this manuscript. There are a number of methods used, of which many are lab-based, but to our knowledge there seems to be no single technique that stands out above the rest, although analytical liquid chromatography, commonly linked with mass spectroscopy is likely to be popular. This review manuscript discusses (a) sample pre-treatment methods such as liquid-liquid extraction (LLE), supercritical fluid extraction (SFE), solid phase extraction (SPE), (b) separation methods such as (TLC), high performance liquid chromatography (HPLC), gas chromatography (GC), and capillary electrophoresis (CE) and (c) others such as ELISA. Further currents trends, advantages and disadvantages and future prospects of these methods have been discussed.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Fausett, Laurene},
doi = {10.1007/s13398-014-0173-7.2},
eprint = {arXiv:1011.1669v3},
isbn = {9780874216561},
issn = {0717-6163},
journal = {Igarss 2014},
keywords = {Bott},
number = {1},
pages = {1--5},
pmid = {15003161},
title = {{Fundamentals of Neural Networks}},
year = {2014}
}
@article{Denoising2010,
author = {Denoising, Multi-image and Removal, Haze and Joshi, Neel and Cohen, Michael F},
file = {::},
isbn = {9781424470235},
journal = {Image (Rochester, N.Y.)},
title = {{Seeing Mt . Rainier : Lucky Imaging for}},
year = {2010}
}
@article{Hautiere2008,
abstract = {The contrast of outdoor images acquired under adverse weather conditions, especially foggy weather, is altered by the scattering of daylight by atmospheric particles. As a consequence, differentmethods have been designed to restore the contrast of these images. However, there is a lack of methodology to assess the performances of the methods or to rate them. Unlike image quality assessment or image restoration areas, there is no easy way to have a reference image, which makes the problem not straightforward to solve. In this paper, an approach is proposed which consists in computing the ratio between the gradient of the visible edges between the image before and after contrast restoration. In this way, an indicator of visibility enhancement is provided based on the concept of visibility level, commonly used in lighting engineering. Finally, the methodology is applied to contrast enhancement assessment and to the comparison of tone-mapping operators.},
author = {Hauti{\`{e}}re, Nicolas and Tarel, Jean Philippe and Aubert, Didier and Dumont, {\'{E}}ric},
doi = {10.5566/ias.v27.p87-95},
issn = {15803139},
journal = {Image Analysis and Stereology},
keywords = {Advanced driver assistance system,Blind assessment,Contrast enhancement,Contrast restoration,Edges segmentation,LIP model,Tone-mapping,Visibility level},
number = {2},
pages = {87--95},
title = {{Blind contrast enhancement assessment by gradient ratioing at visible edges}},
volume = {27},
year = {2008}
}
@incollection{anmoosddifi,
author = {Lu, Wenjun and Sun, Xiaoning and Li, Congli},
booktitle = {Image and Graphics},
doi = {10.1007/978-3-319-21978-3_19},
editor = {Zhang, Yu-Jin},
file = {::},
isbn = {978-3-319-21977-6},
keywords = {Foggy images; Region covariance matrix; Object sal},
pages = {206--217},
publisher = {Springer International Publishing},
series = {Lecture Notes in Computer Science},
title = {{A New Method of Object Saliency Detection in Foggy Images}},
url = {http://dx.doi.org/10.1007/978-3-319-21978-3{\_}19},
volume = {9217},
year = {2015}
}
@article{Vincent1993,
abstract = {Two different formal definitions of gray-scale reconstruction are presented. The use of gray-scale reconstruction in various image processing applications discussed to illustrate the usefulness of this transformation for image filtering and segmentation tasks. The standard parallel and sequential approaches to reconstruction are reviewed. It is shown that their common drawback is their inefficiency on conventional computers. To improve this situation, an algorithm that is based on the notion of regional maxima and makes use of breadth-first image scannings implemented using a queue of pixels is introduced. Its combination with the sequential technique results in a hybrid gray-scale reconstruction algorithm which is an order of magnitude faster than any previously known algorithm},
author = {Vincent, Luc},
doi = {10.1109/83.217222},
file = {::},
isbn = {1057-7149 VO  - 2},
issn = {1057-7149},
journal = {Image Processing, IEEE Transactions on},
keywords = {Algorithm design and analysis,Application software,Filtering,Gray-scale,Image analysis,Image processing,Image reconstruction,Image segmentation,Pixel,Reconstruction algorithms,algorithm,image analysis,image filtering,image processing,image reconstruction,image segmentation,mathematic,mathematical morphology,morphological gray scale reconstruction,parallel reconstruction algorithm,pixels queue,regional maxima,sequential reconstruction algorithm,set theory},
number = {2},
pages = {176--201},
pmid = {18296207},
title = {{Morphological grayscale reconstruction in image analysis: applications and efficient algorithms}},
volume = {2},
year = {1993}
}
@article{7224071,
author = {Yu, Teng and Riaz, I and Piao, Jingchun and Shin, Hyunchul},
doi = {10.1049/iet-ipr.2015.0087},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Yu et al. - 2015 - Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior.pdf:pdf},
issn = {1751-9659},
journal = {Image Processing, IET},
keywords = {Gaussian channels;Gaussian noise;image texture;int},
number = {9},
pages = {725--734},
title = {{Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior}},
volume = {9},
year = {2015}
}
@inproceedings{1040019,
author = {Sural, S and Qian, Gang and Pramanik, S},
booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
doi = {10.1109/ICIP.2002.1040019},
issn = {1522-4880},
keywords = {content-based retrieval;feature extraction;image c},
pages = {II--589--II--592 vol.2},
title = {{Segmentation and histogram generation using the HSV color space for image retrieval}},
volume = {2},
year = {2002}
}
@article{pohl1998review,
author = {Pohl, C and {Van Genderen}, J L},
journal = {Int J Remote Sens},
number = {5},
pages = {823--854},
publisher = {Taylor {\&} Francis},
title = {{Review article Multisensor image fusion in remote sensing: concepts, methods and applications}},
volume = {19},
year = {1998}
}
@article{Neidhart2008,
abstract = {Generalization, Reconstruction, Simplification, Triangulation, Building, LIDAR, Classification, Segmentation The goal of this research is to extract and delineate building ground plans from LIDAR data. Our approach consists of three steps: first of all, the raw point cloud has to be classified into terrain points and off-terrain points. Secondly, the off-terrain points (the potential buildings) have to be aggregated to form connected building blobs. Those blobs that exceed a certain size and have certain characteristics (e.g. consisting of planes) are supposed to be building candidates. For them, in a third step the outline is simplified. This is a generalization task, which has to take the characteristics of buildings into account to produce a meaningful 2D building shape. In the paper, these steps are described in detail. The focus lies on the different possibilities to generalize the building ground plans.},
author = {Neidhart, H and Sester, M},
journal = {International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences},
keywords = {building,classification,generalization,lidar,reconstruction,segmentation,simplification,triangulation},
number = {Part 2},
pages = {405--410},
title = {{Extraction of building ground plans from LiDAR data}},
url = {http://www.ikg.uni-hannover.de/fileadmin/ikg/staff/publications/Konferenzbeitraege{\_}abstract{\_}review/NeidhartSester{\_}isprs2008.pdf},
volume = {XXXVII},
year = {2008}
}
@inproceedings{shangsingle,
address = {Oita, Japan},
author = {Shang, Yuanyuan and Meng, Yue},
booktitle = {International Conference on Artificial Life and Robotics (ICAROB 2015)},
file = {::},
month = {jan},
pages = {10--12},
title = {{Single Image Dehazing on Mobile Device Based on GPU Rendering Technology}},
volume = {1},
year = {2015}
}
@inproceedings{6421247,
address = {Xiamen, China},
author = {Long, Jiao and Shi, Zhenwei and Tang, Wei},
booktitle = {International Conference on Computer Vision in Remote Sensing (CVRS)},
doi = {10.1109/CVRS.2012.6421247},
file = {::},
keywords = {atmospheric techniques;geophysical image processin},
month = {dec},
pages = {132--135},
title = {{Fast haze removal for a single remote sensing image using dark channel prior}},
volume = {1},
year = {2012}
}
@inproceedings{he2012single,
address = {Fremantle, Australia},
author = {He, Renjie and Wang, Zhiyong and Xiong, Hao and Feng, D D},
booktitle = {International Conference on Digital Image Computing Techniques and Applications (DICTA)},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/He et al. - 2012 - Single Image Dehazing with White Balance Correction and Image Decomposition.pdf:pdf},
keywords = {computer vision;image colour analysis;image enhanc},
month = {dec},
pages = {1--7},
title = {{Single Image Dehazing with White Balance Correction and Image Decomposition}},
volume = {1},
year = {2012}
}
@inproceedings{rosas2005simd,
author = {Rosas, R L and de Luca, A and Santillan, F B},
booktitle = {International Conference on Electrical and Electronics Engineering},
doi = {10.1109/ICEEE.2005.1529577},
keywords = {automatic optical inspection;computer vision;edge},
pages = {77--80},
title = {{SIMD architecture for image segmentation using Sobel operators implemented in FPGA technology}},
year = {2005}
}
@inproceedings{iefohanii,
address = {Zhengzhou, China},
author = {Li, Dajian and Jia, Wei and Sun, Wei and Li, Penghui and Zhao, Chunyu and Chen, Xumeng},
booktitle = {International Conference on Electronic Science and Automation Control (ESAC)},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Li et al. - 2015 - Image enhancement focusing on hazy and non-uniform illumination images.pdf:pdf},
month = {aug},
pages = {200--203},
title = {{Image enhancement focusing on hazy and non-uniform illumination images}},
volume = {1},
year = {2015}
}
@inproceedings{lien2012efficient,
address = {Kitakushu, Japan},
author = {Lien, Chih-Yuan and Yang, Fu-Jhong and Huang, Chien-Chuan},
booktitle = {International Conference on Genetic and Evolutionary Computing (ICGEC)},
doi = {10.1109/ICGEC.2012.47},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lien, Yang, Huang - 2012 - An Efficient Image Dehazing Method.pdf:pdf},
keywords = {computational complexity,computational complexity;feature extraction;image,feature extraction,image},
month = {aug},
pages = {348--351},
title = {{An Efficient Image Dehazing Method}},
volume = {6},
year = {2012}
}
@inproceedings{6005597,
abstract = {The technology of image dehazing can only work for haze images, but in batch and real-time processing, only relying on human visual system judge whether the image is haze or non-haze image, is unrealistic, so how to determine whether there are haze or non-haze images is needed to be solved. In this paper, we proposed a method to judge whether a given image is haze. According to the difference between the haze and non-haze images, we extract three eigen values, including image visibility, intensity of dark channel and image contrast, then combine with support vector machine to make judgment of image state which is haze or non-haze, obtaining high recognition rate. Experimental results show that our method is feasible and effective. Our method for bath and real-time processing provide the basis for judging image state, promoting the wide application of image dehazing.},
address = {Hefei, China},
author = {Yu, Xiaoliang and Xiao, Chuangbai and Deng, Mike and Peng, Li},
booktitle = {International Conference on Image and Graphics (ICIG)},
doi = {10.1109/ICIG.2011.22},
file = {::},
isbn = {9780769545417},
keywords = {Distingussish image state,Haze image,Non-haze image,Suppotr vector machine,eigenvalues and eigenfunctions,image classificatio},
month = {aug},
number = {2},
pages = {286--289},
title = {{A Classification Algorithm to Distinguish Image as Haze or Non-haze}},
volume = {1},
year = {2011}
}
@inproceedings{huang1995quantitative,
address = {Washington, USA},
author = {Huang, Qian and Dom, Byron},
booktitle = {International Conference on Image Processing},
month = {oct},
pages = {53--56},
title = {{Quantitative methods of evaluating image segmentation}},
volume = {3},
year = {1995}
}
@inproceedings{zhang2002active,
address = {Annapolis, USA},
author = {Zhang, Yongmian and Ji, Qiang and Looney, C G},
booktitle = {International Conference on Information Fusion},
doi = {10.1109/ICIF.2002.1021215},
keywords = {belief networks;decision theory;sensor fusion;dyna},
pages = {643----650 vol.1},
title = {{Active information fusion for decision making under uncertainty}},
volume = {1},
year = {2002}
}
@inproceedings{7153106,
address = {Tokyo, Japan},
author = {Zhu, Ximei and Li, Ying and Qiao, Yu},
booktitle = {International Conference on Machine Vision Applications (MVA)},
doi = {10.1109/MVA.2015.7153106},
file = {::},
keywords = {image filtering;image restoration;interpolation;re},
month = {may},
pages = {443--446},
title = {{Fast single image dehazing through Edge-Guided Interpolated Filter}},
volume = {1},
year = {2015}
}
@inproceedings{Salhi2014,
abstract = {Traffic Sign Recognition is nowadays widely used in the automobile industry, it provides a good solution for driver assistance systems, it can alert the driver and control or prohibit certain actions, and this can significantly increase driving safety and comfort. This paper presents a study to design, implement and test a method of detecting traffic signs, based on computer vision. The system implemented in this work contains two main modules: the first is the preprocessing module which is based on image processing features such as color segmentation, threshold technique, Gaussian filter, canny edge detection, and contours detection. The second is the detection module based on Polygonal Approximation of digital curves technic applied on contours to identify areas of the scene may contain traffic signs.. The development of the system is performed using the programming language C/C++ with the OpenCV library. The tests are performed on a set of real images of traffic with a complex background, to show the performance of the system being developed.},
author = {Salhi, Abderrahim and Minaoui, Brahim and Fakir, Mohamed},
booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
doi = {10.1109/ICMCS.2014.6911185},
isbn = {978-1-4799-3824-7},
keywords = {Polygonal Approximation,Traffic sign,detection,image processing},
pages = {433--437},
title = {{Robust Automatic Traffic Signs Detection using fast polygonal approximation of digital curves}},
volume = {0},
year = {2014}
}
@inproceedings{5629666,
address = {Ningbo, China},
author = {Zheng, Yufeng and Li, Haitao and Gu, Haiyan and Feng, Haikuan},
booktitle = {International Conference on Multimedia Technology (ICMT)},
doi = {10.1109/ICMULT.2010.5629666},
file = {::},
keywords = {artificial satellites;image processing;parallel al},
month = {oct},
pages = {1--4},
title = {{Research on the Haze Removal Method and Parallel Implementation for HJ -1 Satellite Data}},
volume = {1},
year = {2010}
}
@inproceedings{liu2011research,
address = {Hangzhou, China},
author = {Liu, Qiaoling and Zhang, Hongying and Lin, Maosong and Wu, Yadong},
booktitle = {International Conference on Multimedia Technology (ICMT)},
doi = {10.1109/ICMT.2011.6003078},
file = {::},
keywords = {computer vision;image restoration;natural scenes;a},
month = {jul},
pages = {467--470},
title = {{Research on image dehazing algorithms based on physical model}},
volume = {2},
year = {2011}
}
@inproceedings{wang2012multi,
address = {Nanjing, China},
author = {Wang, Xiaotong and Jin, Xin and Xu, Guanlei and Xu, Xiaogang},
booktitle = {International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE)},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wang et al. - 2012 - A Multi-Scale Decomposition Based Haze Removal Algorithm.pdf:pdf},
month = {jun},
pages = {1--4},
title = {{A Multi-Scale Decomposition Based Haze Removal Algorithm}},
volume = {2},
year = {2012}
}
@inproceedings{7081605,
abstract = {Image dehazing aims at estimating the image information lost caused by the presence of fog, haze and smoke in the scene during acquisition. Degradation causes a loss in contrast and color information, thus enhancement becomes an inevitable task in imaging applications and consumer photography. Color information has been mostly evaluated perceptually along with quality, but no work adresses specifically this aspect. We demonstrate how dehazing model affects color information on simulated and real images. We use a convergence model from perception of transparency to simulate haze on images. We evaluate color loss in terms of angle of hue in IPT color space, saturation in CIE LUV color space and perceived color difference in CIE LAB color space. Results indicate that saturation is critically changed and hue is changed for achromatic colors and blue/yellow colors, where usual image processing space are not showing constant hue lines. We suggest that a correction model based on color transparency perception could help to retrieve color information as an additive layer on dehazing algorithms.},
address = {Bali, Indonesia},
author = {{El Khoury}, Jessica and Thomas, J.-B. Jean Baptiste and Mansouri, Alamin},
booktitle = {International Conference on Signal-Image Technology and Internet-Based Systems (SITIS)},
doi = {10.1109/SITIS.2014.78},
file = {::},
isbn = {9781479979783},
keywords = {color fidelity,colorimetry,contrast enhancement,dehazing,hue,image colour analysis,image enhancement,perception,photograph,saturation},
month = {nov},
pages = {606--613},
title = {{Does Dehazing Model Preserve Color Information?}},
volume = {1},
year = {2014}
}
@inproceedings{7050486,
address = {Bangalore, India},
author = {Kumari, A and Sahdev, S and Sahoo, S K},
booktitle = {International Conference on VLSI Systems, Architecture, Technology and Applications (VLSI-SATA)},
doi = {10.1109/VLSI-SATA.2015.7050486},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kumari, Sahdev, Sahoo - 2015 - Improved single image and video dehazing using morphological operation.pdf:pdf},
keywords = {image filtering;image restoration;natural scenes;v},
month = {jan},
pages = {1--5},
title = {{Improved single image and video dehazing using morphological operation}},
volume = {1},
year = {2015}
}
@inproceedings{5646397,
address = {Yantai, China},
author = {Wei, Pan and Liu, Yangke and Liu, Ying and Wang, Shuhang},
booktitle = {International Congress on Image and Signal Processing (CISP)},
doi = {10.1109/CISP.2010.5646397},
keywords = {computer vision;image restoration;blurring;compute},
month = {oct},
pages = {249--252},
title = {{Dehazing model based on multiple scattering}},
volume = {1},
year = {2010}
}
@inproceedings{7003741,
address = {Dalian, China},
author = {Ji, Xiaoqiang and Cheng, Jiezhang and Bai, Jiaqi and Zhang, Tingting and Wang, Meijiao},
booktitle = {International Congress on Image and Signal Processing (CISP)},
doi = {10.1109/CISP.2014.7003741},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ji et al. - 2014 - Real-time enhancement of the image clarity for traffic video monitoring systems in haze.pdf:pdf},
keywords = {field programmable gate arrays;fog;fuzzy set theor},
month = {oct},
pages = {11--15},
title = {{Real-time enhancement of the image clarity for traffic video monitoring systems in haze}},
volume = {1},
year = {2014}
}
@inproceedings{ho20072,
address = {Orlando, USA},
author = {Ho, P.-G.P. and Chen, Chi Hau},
booktitle = {International Joint Conference on Neural Networks (IJCNN)},
month = {aug},
pages = {316--320},
title = {{2-D Time Series Model Based Support Vector Machine for Remote Sensing Image Segmentation}},
volume = {1},
year = {2007}
}
@article{saggudifferent,
author = {Saggu, Manpreet Kaur and Singh, Satbir},
file = {::},
journal = {International Journal For Technological Research In Engineering},
number = {6},
pages = {566--568},
title = {{DIFFERENT TECHNIQUES TO DEHAZE AN UNDERWATER IMAGE}},
volume = {2},
year = {2015}
}
@article{vala2013review,
author = {Vala, Miss Hetal J and Baxi, Astha},
journal = {International Journal of Advanced Research in Computer Engineering {\&} Technology},
number = {2},
pages = {387--389},
title = {{A review on Otsu image segmentation algorithm}},
volume = {2},
year = {2013}
}
@article{guo2014markov,
author = {Guo, Fan and Tang, Jin and Peng, Hui},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Guo, Tang, Peng - 2014 - A Markov random field model for the restoration of foggy images.pdf:pdf},
journal = {International Journal of Advanced Robotic Systems},
number = {1},
pages = {92},
title = {{A Markov random field model for the restoration of foggy images}},
volume = {11},
year = {2014}
}
@article{Gil2007,
abstract = {Flexible multisensorial systems are a very important issue in the current industry when disassembling and recycling tasks have to be performed. These tasks can be performed by a human operator or by a robot system. In the current paper a robotic system to perform the required tasks is presented. This system takes into consideration the distribution of the necessary tasks to perform the disassembly of a component using several robots in a parallel or in a cooperative way. The algorithm proposed to distribute the task among robots takes into consideration the characteristics of each task and the sequence that needs to be followed to perform the required disassembly of the product. Furthermore, this paper presents a disassembly system based on a sensorized cooperative robots interaction framework for the planning of movements and detections of objects in the disassembly tasks. To determine the sequence of the disassembly of some products, a new strategy to distribute a set of tasks among robots is presented. Subsequently, the visual detection system used for detecting targets and characteristics is described. To carry out this detection process, different well known strategies, such as matching templates, polygonal approach and edge detection, are applied. Finally, a visual-force control system has been implemented in order to track disassembly trajectories. An important aspect of this system is the processing of the sensorial information in order to guarantee coherence. This aspect allows the application of both sensors, visual and force sensors, co-ordinately to disassembly tasks. The proposed system is validated by experiments using several types of components such as the covers of batteries and electronic circuits from toys, and drives and screws from PCs.},
author = {Gil, P. and Pomares, J. and Diaz, S. vT. Puente C. and Candelas, F. and Torres, F.},
doi = {10.1080/09511920601143169},
isbn = {0951-192X},
issn = {0951-192X},
journal = {International Journal of Computer Integrated Manufacturing},
number = {8},
pages = {757--772},
title = {{Flexible multi-sensorial system for automatic disassembly using cooperative robots}},
url = {http://www.tandfonline.com/doi/abs/10.1080/09511920601143169},
volume = {20},
year = {2007}
}
@article{singh2010study,
author = {Singh, Krishna Kant and Singh, Akansha},
journal = {International Journal of Computer Science Issues},
month = {sep},
number = {5},
pages = {414--417},
publisher = {Citeseer},
title = {{A study of image segmentation algorithms for different types of images}},
volume = {7},
year = {2010}
}
@article{raey1,
author = {Nishino, Ko and Kratz, Louis and Lombardi, Stephen},
file = {::},
issn = {0920-5691},
journal = {International Journal of Computer Vision},
keywords = {Defogging; Dehazing; Scene albedo; Scene depth; Ma},
number = {3},
pages = {263--278},
publisher = {Springer US},
title = {{Bayesian Defogging}},
volume = {98},
year = {2012}
}
@article{huo2015,
author = {Huo, Bingquan and Yin, Fengling and Polytechnic, Binzhou},
file = {::},
journal = {International Journal of Multimedia and Ubiquitous Engineering},
keywords = {dark channel prior,estimation,image dehaze,image segmentation},
number = {3},
pages = {13--22},
title = {{Image Dehazing with Dark Channel Prior and Novel Estimation Model}},
volume = {10},
year = {2015}
}
@article{gundawar2014improved,
author = {Gundawar, Nitish and Baru, V B},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gundawar, Baru - 2014 - Improved Single image dehazing by fusion.pdf:pdf},
journal = {International Journal of Research in Engineering and Technology (IJRET)},
number = {5},
pages = {432--437},
publisher = {IJRET},
title = {{Improved Single image dehazing by fusion}},
volume = {3},
year = {2014}
}
@article{samma2009adaptation,
author = {Samma, A S B and Salam, R A},
journal = {International Journal of Signal Processing},
number = {4},
pages = {270--274},
title = {{Adaptation of k-means algorithm for image segmentation}},
volume = {5},
year = {2009}
}
@article{bb54683,
abstract = {Many intuitively appealing methods have been suggested for clustering data, however, interpretation of their results has been hindered by the lack of objective criteria. This article proposes several criteria which isolate specific aspects of the performance of a method, such as its retrieval of inherent structure, its sensitivity to resampling and the stability of its results in the light of new data. These criteria depend on a measure of similarity between two different clusterings of the same set of data; the measure essentially considers how each pair of data points is assigned in each clustering.},
author = {Rand, William M},
issn = {01621459},
journal = {J Am Stat Assoc},
number = {336},
pages = {pp. 846----850},
publisher = {American Statistical Association},
title = {{Objective Criteria for the Evaluation of Clustering Methods}},
volume = {66},
year = {1971}
}
@article{FowlkesE1983Method_withcomments,
author = {Fowlkes, E B and Mallows, C L},
journal = {J Am Stat Assoc},
number = {383},
pages = {553--569},
title = {{A method for comparing two hierarchical clusterings}},
volume = {78},
year = {1983}
}
@article{optimizedcontrastenhancement,
abstract = {A fast and optimized dehazing algorithm for hazy images and videos is proposed in this work. Based on the observation that a hazy image exhibits low contrast in general, we restore the hazy image by enhancing its contrast. However, the overcompensation of the degraded contrast may truncate pixel values and cause information loss. Therefore, we formulate a cost function that consists of the contrast term and the information loss term. By minimizing the cost function, the proposed algorithm enhances the contrast and preserves the information optimally. Moreover, we extend the static image dehazing algorithm to real-time video dehazing. We reduce flickering artifacts in a dehazed video sequence by making transmission values temporally coherent. Experimental results show that the proposed algorithm effectively removes haze and is sufficiently fast for real-time dehazing applications. {\textcopyright} 2013 Elsevier Inc. All rights reserved.},
address = {Orlando, FL, USA},
author = {Kim, Jin-Hwan Hwan and Jang, Won-Dong Dong and Sim, Jae-Young Young and Kim, Chang-Su Su},
doi = {10.1016/j.jvcir.2013.02.004},
file = {::},
isbn = {1047-3203},
issn = {1047-3203},
journal = {J. Vis. Comun. Image Represent.},
keywords = {Atmospheric light estimation,Contrast enhancement,Image dehazing,Image enhancement,Image restoration,Optimized dehazing,Temporal coherence,Video dehazing},
number = {3},
pages = {410--425},
publisher = {Academic Press, Inc.},
title = {{Optimized Contrast Enhancement for Real-time Image and Video Dehazing}},
volume = {24},
year = {2013}
}
@article{guoO,
author = {Guo, Fan and Tang, Jin and Cai, Zi-xing},
doi = {10.1007/s11771-014-1938-z},
issn = {2095-2899},
journal = {Journal of Central South University},
number = {1},
pages = {272--286},
publisher = {Central South University},
title = {{Objective measurement for image defogging algorithms}},
volume = {21},
year = {2014}
}
@article{zhang2013single,
author = {Zhang, Hongying and Liu, Qiaolin and YANG, Fan and WU, Yadong},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Zhang et al. - 2013 - Single Image Dehazing Combining Physics Model based and Non-physics Model based Methods.pdf:pdf},
journal = {Journal of Computational Information Systems (IJCA)},
number = {4},
pages = {1623--1631},
title = {{Single Image Dehazing Combining Physics Model based and Non-physics Model based Methods}},
volume = {9},
year = {2013}
}
@article{survey_thresholding,
abstract = {We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nondestructive testing and document image applications. � 2004 SPIE and IS{\&}T.},
author = {Sezgin, Mehmet and Others and Sankur, Bu�lent},
doi = {10.1117/1.1631315},
isbn = {1017-9909},
journal = {Journal of Electronic Imaging},
number = {1},
pages = {146--168},
publisher = {International Society for Optics and Photonics},
title = {{Survey over image thresholding techniques and quantitative performance evaluation}},
url = {http://dx.doi.org/10.1117/1.1631315},
volume = {13},
year = {2004}
}
@article{liao2001fast,
author = {Liao, P S and Chen, T S and Chung, P C},
journal = {Journal of information science and engineering},
number = {5},
pages = {713--728},
publisher = {Institute of Information Science Academia Sinica},
title = {{A fast algorithm for multilevel thresholding}},
volume = {17},
year = {2001}
}
@article{JMMjmm0804432438,
abstract = {Based on dark channel priority, the paper proposes an improved defogging algorithm of single image which can defog the foggy images rapidly. The algorithm in the paper applies the method combining adaptive median filter and bilateral filter to figure out clear dark channel on the edge. And the algorithm is based on the physical model of foggy images to estimate transmission. Compared with the traditional algorithm, the estimated transmission is detailed and clear, and has no need to be optimized, which not only overcomes the disadvantages of traditional algorithm using plenty of time to optimize transmission, but also reduces the complexity of the algorithm. The experimental results indicate that the algorithm realizes rapid and high-quality defogging on single image},
author = {Zhen, Chen and Jihong, Shen and Roth, Peter and Chen, Zhen and Shen, Jihong and Roth, Peter},
doi = {10.4304/jmm.8.4.432-438},
file = {::},
isbn = {9783037858066},
issn = {17962048},
journal = {Journal of Multimedia},
keywords = {Adaptive median filter,Bilateral filter,Dark Channel Priorit,Dark channel priority,Defogging,Image Restoration,Image restoration},
number = {4},
pages = {432--438},
title = {{Single Image Defogging Algorithm based on Dark Channel Priority}},
url = {http://ojs.academypublisher.com/index.php/jmm/article/view/jmm0804432438},
volume = {8},
year = {2013}
}
@article{roberts2002review,
author = {Roberts, Craig A},
journal = {Journal of Transportation Engineering},
number = {4},
pages = {392--393},
publisher = {American Society of Civil Engineers},
title = {{Review of Sensor technologies and data requirements for ITS by Lawrence A. Klein}},
volume = {128},
year = {2002}
}
@article{Shiau2014445,
author = {Shiau, Y.-H. and Chen, P.-Y. and Yang, H.-Y. and Chen, C.-H. and Wang, S.-S.},
doi = {http://dx.doi.org/10.1016/j.jvcir.2013.12.011},
file = {::},
issn = {1047-3203},
journal = {Journal of Visual Communication and Image Representation},
keywords = {Dehaze},
number = {2},
pages = {445--453},
title = {{Weighted haze removal method with halo prevention}},
url = {http://www.sciencedirect.com/science/article/pii/S1047320313002253},
volume = {25},
year = {2014}
}
@article{meyer1990morphological,
author = {Meyer, F and Beucher, S},
journal = {Journal of visual communication and image representation (JVCIR)},
number = {1},
pages = {21--46},
publisher = {Elsevier},
title = {{Morphological segmentation}},
volume = {1},
year = {1990}
}
@inproceedings{7103720,
address = {Mokpo, South Korea},
author = {Kim, Sangkyoon and Park, Soonyoung and Choi, Kyoungho},
booktitle = {Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)},
doi = {10.1109/FCV.2015.7103720},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Kim, Park, Choi - 2015 - A system architecture for real time traffic monitoring in foggy video.pdf:pdf},
keywords = {computerised monitoring;multi-threading;object det},
month = {jan},
pages = {1--4},
title = {{A system architecture for real time traffic monitoring in foggy video}},
volume = {1},
year = {2015}
}
@article{msidboawf,
author = {Wang, Wei and Li, Wenhui and Wang, Qingji Guan Wei and Liand, Wenhui and Qi, Miao},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wang et al. - 2015 - Multiscale Single Image Dehazing Based on Adaptive Wavelet Fusion.pdf:pdf},
journal = {Mathematical Problems in Engineering},
number = {14},
pages = {14},
title = {{Multiscale Single Image Dehazing Based on Adaptive Wavelet Fusion}},
volume = {1},
year = {2015}
}
@article{Greenberg1998,
abstract = {The Ford-Fulkerson max labeling algorithmm3, 4 w as introduced in the mid-1950's, and became the seminal work that is still applicable. The material presented in this note is taken from their bookk55. We are given a simple network with two speciied nodes: source s and sink t. Since the network is assumed to be simple, an arc is identiied by its endpoints: x; y is the arc from node x to node y. A o w across arc x; y is denoted by fx; y, and the arc's capacity i s cx; y. The ow m ust satisfy 0 fx; y cx; y. The algorithm has two parts, which F ord and Fulkerson called Routine A and Routine B, respectively. The is a labeling process that searches for a augmenting path i.e., a path from s to t for which f f c along all forward arcs and f f 0 along all backward arcs. If Routine A a augmenting path, Routine B c hanges the accordingly. Otherwise, no augmenting path exists, and optimality of the current t o w is ensured by their theorem: Theorem. A A o w f has maximum value if, and only if, there is no augmenting path with},
author = {Greenberg, Harvey J},
journal = {Mathematical Programming Glossary},
pages = {1--5},
title = {{Ford-Fulkerson Max Flow Labeling Algorithm}},
year = {1998}
}
@article{Eklund2013,
abstract = {Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.},
author = {Eklund, Anders and Dufort, Paul and Forsberg, Daniel and LaConte, Stephen M.},
doi = {10.1016/j.media.2013.05.008},
issn = {13618415},
journal = {Medical Image Analysis},
keywords = {CUDA,Graphics processing unit (GPU),Image processing,Image reconstruction,Medical imaging},
month = {dec},
number = {8},
pages = {1073--1094},
pmid = {23906631},
title = {{Medical image processing on the GPU – Past, present and future}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23906631 http://linkinghub.elsevier.com/retrieve/pii/S1361841513000820},
volume = {17},
year = {2013}
}
@inproceedings{martinez1999retinal,
author = {Mart$\backslash$'$\backslash$inez-P{\'{e}}rez, M Elena and Hughes, Alun D and Stanton, Alice V and Thom, Simon A and Bharath, Anil A and Parker, Kim H},
booktitle = {Medical Image Computing and Computer-Assisted Intervention--MICCAI�99},
pages = {90--97},
title = {{Retinal blood vessel segmentation by means of scale-space analysis and region growing}},
year = {1999}
}
@inproceedings{Gibson2010,
abstract = {This paper proposes a novel method for single image dehazing that operates at a faster speed than current methods for implementation in video enhancements. We provide a comparison of our proposed dehazing method with current state of the art methods. We then consider the effect of compression by investigating the blocking and ringing artifacts in cases of applying any dehazing method before or after compression. Based on an investigation with the JPEG model, we conclude that the best dehazing performance (with less artifacts) is achieved if the dehazing is applied before compression. Simulations for both JPEG images and H.264 compressed sequences validate our conclusion.},
author = {Gibson, Kristofor and V{\~{o}}, Dũng and Nguyen, Truong},
booktitle = {MTS/IEEE Seattle, OCEANS 2010},
doi = {10.1109/OCEANS.2010.5664479},
isbn = {9781424443321},
title = {{An investigation in dehazing compressed images and video}},
year = {2010}
}
@inproceedings{6975965,
address = {Xiamen, China},
author = {Qian, Xiaoyan and Han, Lei},
booktitle = {Natural Computation (ICNC), 2014 10th International Conference on},
doi = {10.1109/ICNC.2014.6975965},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Qian, Han - 2014 - Fast image dehazing algorithm based on multiple filters.pdf:pdf},
keywords = {atmospheric techniques;filtering theory;geophysica},
month = {aug},
pages = {937--941},
title = {{Fast image dehazing algorithm based on multiple filters}},
volume = {1},
year = {2014}
}
@article{li2015,
abstract = {In this paper, we present a simple but effective image prior, change of detail (CoD) prior, to remove haze from a single input image. The CoD prior is developed based on the multiple scattering phenomenon in the propagation of light. By adopting the CoD prior in an image model, the thickness of the haze can be estimated effectively to recover a high quality, haze-free image. Our method is stable to image local regions containing objects in different depths. In addition, since the CoD prior is mostly sensitive to local details of the image rather than color and intensity, our method is able to handle both color and grayscale images. Our experiments showed that the proposed method achieved better results than several state-of-the-art methods, and it can be implemented very quickly.},
author = {Li, Jiafeng and Zhang, Hong and Yuan, Ding and Sun, Mingui},
doi = {10.1016/j.neucom.2015.01.026},
file = {::},
issn = {09252312},
journal = {Neurocomputing},
keywords = {Change of details,Image enhancement,Image restoration,Multiple scattering,Single image dehazing},
pages = {1--11},
publisher = {Elsevier},
title = {{Single image dehazing using the change of detail prior}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0925231215000478 http://dx.doi.org/10.1016/j.neucom.2015.01.026},
volume = {156},
year = {2015}
}
@article{wang2015718,
author = {Wang, Jin-Bao and He, Ning and Zhang, Lu-Lu and Lu, Ke},
doi = {http://dx.doi.org/10.1016/j.neucom.2014.08.005},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wang et al. - 2015 - Single image dehazing with a physical model and dark channel prior.pdf:pdf},
issn = {0925-2312},
journal = {Neurocomputing},
keywords = {Dehaze},
pages = {718--728},
title = {{Single image dehazing with a physical model and dark channel prior}},
url = {http://www.sciencedirect.com/science/article/pii/S0925231214010157},
volume = {149, Part },
year = {2015}
}
@article{Vinkey,
author = {Sahu, Vinkey and {Vinkey Sahu}, Mrs.Manjusha Singh},
file = {::},
journal = {nternational Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC)},
keywords = {- image dehazing,{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_},{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_}{\_},dark channel prior,fattal method,fusion dehazing,haze formation model,polarization filtering},
number = {2},
pages = {85--88},
title = {{A Survey Paper On Single Image Dehazing}},
volume = {3},
year = {2015}
}
@inproceedings{5664428,
address = {Seattle, USA},
author = {Carlevaris-Bianco, N and Mohan, A and Eustice, R M},
booktitle = {OCEANS 2010},
doi = {10.1109/OCEANS.2010.5664428},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Carlevaris-Bianco, Mohan, Eustice - 2010 - Initial results in underwater single image dehazing.pdf:pdf},
keywords = {image colour analysis;light scattering;light trans},
month = {sep},
pages = {1--8},
title = {{Initial results in underwater single image dehazing}},
volume = {1},
year = {2010}
}
@inproceedings{5664479,
address = {Seattle, USA},
author = {Gibson, K and Vo, Dung and Nguyen, Truong},
booktitle = {OCEANS 2010},
doi = {10.1109/OCEANS.2010.5664479},
file = {::},
keywords = {code standards;image coding;image enhancement;imag},
month = {sep},
pages = {1--8},
title = {{An investigation in dehazing compressed images and video}},
volume = {1},
year = {2010}
}
@article{Lin2012,
author = {Lin, Zheqi and Wang, Xuansheng},
file = {::},
journal = {Open Journal of Applied Sciences},
keywords = {- image dehazing,applications of images with,changing scenes,dark channel prior,difficult to meet with,down-sampling,guided filter,images,in order to overcome,real-time,satisfied and make them,the drawback of multiple},
pages = {123--127},
title = {{Dehazing for Image and Video Using Guided Filter}},
volume = {2},
year = {2012}
}
@article{yeh:13,
abstract = {Images/videos captured from optical devices are usually degraded by turbid media such as haze, smoke, fog, rain and snow. Haze is the most common problem in outdoor scenes because of the atmosphere conditions. This paper proposes a novel single image-based dehazing framework to remove haze artifacts from images, where we propose two novel image priors, called the pixel-based dark channel prior and the pixel-based bright channel prior. Based on the two priors with the haze optical model, we propose to estimate atmospheric light via haze density analysis. We can then estimate transmission map, followed by refining it via the bilateral filter. As a result, high-quality haze-free images can be recovered with lower computational complexity compared with the state-of-the-art approach based on patch-based dark channel prior.},
author = {Yeh, Chia-Hung and Kang, Li-Wei and Lee, Ming-Sui and Lin, Cheng-Yang},
doi = {10.1364/OE.21.027127},
file = {::},
journal = {Opt. Express},
keywords = {Image enhancement; Image reconstruction-restoratio},
month = {nov},
number = {22},
pages = {27127--27141},
publisher = {OSA},
title = {{Haze effect removal from image via haze density estimation in optical model}},
url = {http://www.opticsexpress.org/abstract.cfm?URI=oe-21-22-27127},
volume = {21},
year = {2013}
}
@article{Fang:14,
abstract = {The analysis of polarized filtered images has been proven useful in image dehazing. However, the current polarization-based dehazing algorithms are based on the assumption that the polarization is only associated with the airlight. This assumption does not hold up well in practice since both object radiance and airlight contribute to the polarization. In this study, a new polarization hazy imaging model is presented, which considers the joint polarization effects of the airlight and the object radiance in the imaging process. In addition, an effective method to synthesize the optimal polarized-difference (PD) image is introduced. Then, a decorrelation-based scheme is proposed to estimate the degree of polarization for the object from the polarized image input. After that, the haze-free image can be recovered based on the new polarization hazy imaging model. The qualitative and quantitative experimental results verify the effectiveness of this new dehazing scheme. As a by-product, this scheme also provides additional polarization properties of the objects in the image, which can be used in extended applications, such as scene segmentation and object recognition.},
author = {Fang, Shuai and Xia, XiuShan and Huo, Xing and Chen, ChangWen},
doi = {10.1364/OE.22.019523},
file = {::},
issn = {1094-4087},
journal = {Opt. Express},
keywords = {Image enhancement,Image reconstruction-restoratio},
month = {aug},
number = {16},
pages = {19523--19537},
pmid = {25321035},
publisher = {OSA},
title = {{Image dehazing using polarization effects of objects and airlight}},
url = {http://www.opticsexpress.org/abstract.cfm?URI=oe-22-16-19523 https://www.osapublishing.org/oe/abstract.cfm?uri=oe-22-16-19523},
volume = {22},
year = {2014}
}
@article{713819629,
author = {Cox, L J},
doi = {10.1080/713819629},
journal = {Optica Acta: International Journal of Optics},
number = {7},
pages = {779},
title = {{Optics of the Atmosphere-Scattering by Molecules and Particles}},
volume = {24},
year = {1977}
}

@Article{Zhu2017,
  author   = {Zhu, Mingzhu and He, Bingwei},
  title    = {{Dehazing via graph cut}},
  journal  = {Optical Engineering},
  year     = {2017},
  volume   = {56},
  pages    = {113105--113111},
  abstract = {Transmission map estimation is one of the most important parts of single image dehazing, which is known as an under-constraint problem. Various assumptions, some have been summarized as priors or models, are proposed to solve this problem. However, many previous methods cannot honestly reflect their theory in the results, due to not considering all of the employed assumptions simultaneously. Meanwhile, most other methods avoiding this defect are with inappropriate assumptions. We try to solve this problem by proposing a method that simultaneously considers the dark channel prior and the piecewise smoothness assumption. It is achieved by minimizing an energy function based on all of the employed assumptions. To maintain a reasonable run time, the minimization problem is mapped into a graph cut problem with a specific graph build strategy. The method is compared with state-of-the-art methods on both synthetic and natural images. Experimental results show that the proposal is promising for haze removal quality.},
  url      = {http://dx.doi.org/10.1117/1.OE.56.11.113105},
}
@article{Yang2006,
abstract = {We demonstrate a modified contrast-stretching method to enhance a non-uniformly illuminated image. Low-frequency information of the image is processed by the conventional manipulation, while the high-frequency information is exaggerated by the log transformation. Characteristics in the picture are better enhanced compared to the traditional algorithm. ?? 2004 Elsevier Ltd. All rights reserved.},
author = {Yang, Ching Chung},
doi = {10.1016/j.optlastec.2004.11.009},
issn = {00303992},
journal = {Optics and Laser Technology},
keywords = {Contrast,Image enhancement},
number = {3},
pages = {196--201},
title = {{Image enhancement by modified contrast-stretching manipulation}},
volume = {38},
year = {2006}
}
@article{Ge20153245,
author = {Ge, Guangyi and Wei, Zhenzhong and Zhao, Jingxin},
doi = {http://dx.doi.org/10.1016/j.ijleo.2015.07.138},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ge, Wei, Zhao - 2015 - Fast single-image dehazing using linear transformation.pdf:pdf},
issn = {0030-4026},
journal = {Optik - International Journal for Light and Electron Optics},
keywords = {Dehaze},
number = {21},
pages = {3245--3252},
title = {{Fast single-image dehazing using linear transformation}},
url = {http://www.sciencedirect.com/science/article/pii/S0030402615006907},
volume = {126},
year = {2015}
}
@article{Qi20153400,
author = {Qi, Miao and Hao, Qiaohong and Guan, Qingji and Kong, Jun and Zhang, You},
doi = {http://dx.doi.org/10.1016/j.ijleo.2015.07.114},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Qi et al. - 2015 - Image dehazing based on structure preserving.pdf:pdf},
issn = {0030-4026},
journal = {Optik - International Journal for Light and Electron Optics},
keywords = {Image dehazing},
number = {22},
pages = {3400--3406},
title = {{Image dehazing based on structure preserving}},
url = {http://www.sciencedirect.com/science/article/pii/S003040261500666X},
volume = {126},
year = {2015}
}
@article{shi20143868,
author = {Shi, Zhenwei and Long, Jiao and Tang, Wei and Zhang, Changshui},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Shi et al. - 2014 - Single image dehazing in inhomogeneous atmosphere.pdf:pdf},
journal = {Optik - International Journal for Light and Electron Optics},
number = {15},
pages = {3868--3875},
title = {{Single image dehazing in inhomogeneous atmosphere}},
volume = {125},
year = {2014}
}
@phdthesis{K.2010,
abstract = {Current vision systems are designed to perform in clear weather. Needless to say, in any outdoor application, there is no escape from bad weather. Ultimately, computer vision systems must include mechanisms that enable them to function (even if somewhat less reliably) in the presence of haze, fog, rain, hail and snow. We begin by studying the visual manifestations of different weather conditions. For this, we draw on what is already known about atmospheric optics, and identify effects caused by bad weather that can be turned to our advantage; we are not only interested in what bad weather does to vision but also what it can do for vision.},
author = {K., He and J., Sun and X., Tang},
booktitle = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
file = {::;::},
isbn = {9781424439911},
number = {12},
pages = {2341--2353},
title = {{Single image haze removal using dark channel prior. Single image haze removal using dark channel prior.}},
url = {http://portal.acm.org/citation.cfm?id=997554},
volume = {33},
year = {2010}
}
@article{VanDroogenbroeck,
abstract = {This paper presents a general algorithm that performs basic mathematical morphology operations, like erosions and openings, with any arbitrary shaped structuring element in an efficient way. It is shown that our algorithm has a lower or equal complexity but better computing time than all comparable known methods.},
address = {New York, NY, USA},
author = {{Van Droogenbroeck}, Marc and Talbot, Hugues},
doi = {10.1016/S0167-8655(96)00113-4},
file = {::},
issn = {0167-8655},
journal = {Pattern Recogn. Lett.},
keywords = {Algorithm,Arbitrary shape,Mathematical morphology,algorithm,arbitrary shape,mathematical morphology},
month = {dec},
number = {14},
pages = {1451--1460},
publisher = {Elsevier Science Inc.},
title = {{Fast Computation of Morphological Operations with Arbitrary Structuring Elements}},
url = {http://dx.doi.org/10.1016/S0167-8655(96)00113-4},
volume = {17},
year = {1996}
}
@article{sauvola2000adaptive,
author = {Sauvola, Jaakko and Pietik{\"{a}}inen, Matti},
journal = {Pattern recognition},
number = {2},
pages = {225--236},
publisher = {Elsevier},
title = {{Adaptive document image binarization}},
volume = {33},
year = {2000}
}
@article{Chen20082731,
author = {Chen, Chi Hau and Ho, Pei-Gee Peter},
doi = {http://dx.doi.org/10.1016/j.patcog.2008.04.013},
issn = {0031-3203},
journal = {Pattern Recognition},
keywords = {Remote sensing},
number = {9},
pages = {2731--2741},
title = {{Statistical pattern recognition in remote sensing}},
volume = {41},
year = {2008}
}
@article{li2002multifocus,
author = {Li, S and Kwok, J T and Wang, Y},
journal = {Pattern Recognition Letters},
number = {8},
pages = {985--997},
publisher = {Elsevier},
title = {{Multifocus image fusion using artificial neural networks}},
volume = {23},
year = {2002}
}
@article{Ng20061644,
author = {Ng, Hui-Fuang},
doi = {http://dx.doi.org/10.1016/j.patrec.2006.03.009},
issn = {0167-8655},
journal = {Pattern Recognition Letters},
keywords = {Automatic thresholding,Defect detection},
number = {14},
pages = {1644--1649},
title = {{Automatic thresholding for defect detection}},
url = {http://www.sciencedirect.com/science/article/pii/S016786550600119X},
volume = {27},
year = {2006}
}
@inproceedings{5344078,
address = {Nanjing, China},
author = {Zhu, Ningbo and Wang, Gang and Yang, Gaobo and Dai, Weiming},
booktitle = {Pattern Recognition, 2009. CCPR 2009. Chinese Conference on},
doi = {10.1109/CCPR.2009.5344078},
keywords = {computational complexity;image segmentation;statis},
month = {nov},
pages = {1--5},
title = {{A Fast 2D Otsu Thresholding Algorithm Based on Improved Histogram}},
volume = {1},
year = {2009}
}
@article{Hu2014,
abstract = {In this report, we present the design of a holey fiber structure with six air holes in the cladding and a transverse microfluidic channel intersecting the silica core for the purpose of liquid detection. The simulation results suggest an improved refractive index sensitivity of the proposed structure compared to that of a single mode fiber with a similar transverse channel. The proposed holey fiber structures are fabricated and characterized. {\textcopyright} 2014 Photonics Society of Poland.},
author = {Hu, Dora Juan Juan and Cui, Ying and Lim, Jun Long and Shum, Perry Ping and Humbert, Georges and Auguste, Jean Louis and Wang, Yixin},
doi = {10.4302/plp.2014.2.02},
file = {::},
issn = {20802242},
journal = {Photonics Letters of Poland},
number = {2},
pages = {53--55},
title = {{Design and fabrication of a holey fiber microfluidic device with transverse micro-channel}},
volume = {6},
year = {2014}
}
@article{ojeda,
author = {Ojeda-Castaneda, Jorge and Y{\'{e}}pez-Vidal, E and Garc$\backslash$'$\backslash$ia-Almanza, E},
journal = {Photonics Letters of Poland},
number = {4},
pages = {pp----162},
title = {{Complex amplitude filters for extended depth of field}},
volume = {2},
year = {2010}
}
@inproceedings{zhang1997comparison,
address = {San Jose, USA},
author = {Zhang, Yujin and Liu, Zhong W and He, Yun},
booktitle = {Photonics West'98 Electronic Imaging},
pages = {371--382},
title = {{Comparison and improvement of color-based image retrieval techniques}},
year = {1997}
}
@article{McCartney1977,
abstract = {An overview of atmospheric scattering is provided, taking into account the role of scattering, the elements of radiometry and photometry, the principal characteristics of scattering, scattering in the atmosphere, visibility along an atmospheric path, and literature of atmospheric optics. The structure and composition of the gas atmosphere are examined, giving attention to factors from kinetic theory, the atmospheric envelope, models of the atmosphere, measures of the overhead atmosphere, and quantitative treatments of optical paths. Particles in the atmosphere are considered along with Rayleigh scattering by molecules, Mie scattering by monodispersions, and Mie scattering by atmospheric polydispersions.},
author = {McCartney, E. J. and Hall, Freeman F.},
doi = {10.1063/1.3037551},
isbn = {0471015261},
issn = {0031-9228},
journal = {Physics Today},
number = {5},
pages = {76--77},
title = {{{\textless}i{\textgreater}Optics of the Atmosphere: Scattering by Molecules and Particles{\textless}/i{\textgreater}}},
url = {http://physicstoday.scitation.org/doi/10.1063/1.3037551},
volume = {30},
year = {1977}
}
@article{Hershberger1992,
abstract = {We analyze the line simplification algorithm reported by Douglas and Peucker and show that its worst case is quadratic in n, the number of input points. Then we give a algorithm, based on path hulls, that uses the geometric structure of the problem to attain a worst-case running time proportional to n log 2 n, which is the best case of the Douglas algorithm. We give complete C code and compare the two algorithms theoretically, by operation counts, and practically, by machine timings.},
author = {Hershberger, J E and Snoeyink, Jack},
doi = {10.1.1.17.6932},
journal = {Proc. 5th Intl. Symp. on Spatial Data Handling},
pages = {134--143},
title = {{Speeding up the Douglas-Peucker line-simplification algorithm}},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.6932{\&}rep=rep1{\&}type=pdf{\%}5Cnhttp://www.bowdoin.edu/{~}ltoma/teaching/cs350/spring06/Lecture-Handouts/hershberger92speeding.pdf},
year = {1992}
}
@inproceedings{doi:10.1117/12.906773,
abstract = {Images of outdoor scenes often contain degradation due to haze, resulting in contrast reduction and color fading. For many reasons one may need to remove these effects. Unfortunately, haze removal is a difficult problem due the inherent ambiguity between the haze and the underlying scene. Furthermore, all images contain some noise due to sensor (measurement) error that can be amplified in the haze removal process if ignored. A number of methods have been proposed for haze removal from images. Existing literature that has also addressed the issue of noise has relied on multiple images either for denoising prior to dehazing1 or in the dehazing process itself.2, 3 However, multiple images are not always available. Recent single image approaches, one of the most successful being the "dark channel prior",4 have not yet considered the issue of noise. Accordingly, in this paper we propose two methods for removing both haze and noise from a single image. The first approach is to denoise the image prior to dehazing. This serial approach essentially treats haze and noise separately, and so a second approach is proposed to simultaneously denoise and dehaze using an iterative, adaptive, non-parametric regression method. Experimental results for both methods are then compared. Our findings show that when the noise level is precisely known a priori, simply denoising prior to dehazing performs well. When the noise level is not given, latent errors from either "under"-denoising or "over"-denoising can be amplified, and in this situation, the iterative approach can yield superior results.},
address = {Burlingame, USA},
author = {Matlin, Erik and Milanfar, Peyman},
booktitle = {Proc. SPIE},
doi = {10.1117/12.906773},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Matlin, Milanfar - 2012 - Removal of haze and noise from a single image.pdf:pdf},
month = {jan},
pages = {82960T--82960T--12},
title = {{Removal of haze and noise from a single image}},
url = {http://dx.doi.org/10.1117/12.906773},
volume = {8296},
year = {2012}
}
@misc{doi:10.1117/12.2087774,
abstract = {
As the effect of atmospheric particles scattering, the video image captured by outdoor surveillance system has low contrast and brightness, which directly affects the application value of the system. The traditional defogging technology is mostly studied by software for the defogging algorithms of the single frame image. Moreover, the algorithms have large computation and high time complexity. Then, the defogging technology of video image based on Digital Signal Processing (DSP) has the problem of complex peripheral circuit. It can�t be realized in real-time processing, and it�s hard to debug and upgrade. In this paper, with the improved dark channel prior algorithm, we propose a kind of defogging technology of video image based on Field Programmable Gate Array (FPGA). Compared to the traditional defogging methods, the video image with high resolution can be processed in real-time. Furthermore, the function modules of the system have been designed by hardware description language. At last, the results show that the defogging system based on FPGA can process the video image with minimum resolution of 640�480 in real-time. After defogging, the brightness and contrast of video image are improved effectively. Therefore, the defogging technology proposed in the paper has a great variety of applications including aviation, forest fire prevention, national security and other important surveillance.
},
author = {Liu, Shuo and Piao, Yan},
booktitle = {Proc. SPIE},
doi = {10.1117/12.2087774},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Liu, Piao - 2015 - Research on defogging technology of video image based on FPGA.pdf:pdf},
pages = {95210Q--95210Q--8},
title = {{Research on defogging technology of video image based on FPGA}},
url = {http://dx.doi.org/10.1117/12.2087774},
volume = {9521},
year = {2015}
}
@misc{12.420902,
abstract = {The appearance of skin colors in the images depends among other things, on the camera, the calibration of the camera, and the illumination under which the image was taken. In this study, we investigate how the skin colors appear in the chromaticity coordinates of different color spaces like HSV/HSL, normalized rgb, YES and TSL. For this purpose, we have taken images of faces under 16 different illumination/camera calibration conditions using simulated illuminants (Horizon, A, fluorescent TL84 and daylight) with different RGB cameras (1CCD web cameras and a 3CCD camera). In the making of this series of 16 images, first the selected camera was calibrated to one of the four light sources and an image was taken. After that the light source was changed to the other light sources and at each time the person was imaged. The process was repeated to the other two light sources. The same procedure was done for all four light sources and for each camera. The skin regions were extracted from these images and this skin data was then converted to different color spaces. We inspected how the chromaticities of different skin color groups in these color spaces overlap in images taken in all 16 different cases and only in those cases in which the selected camera was calibrated to the current illuminant. These investigations were also made between different cameras. In addition to this, we examined the overlapping of all skin chromaticities from the different skin color groups between cameras.},
author = {Martinkauppi, J Birgitta and Soriano, Maricor N and Laaksonen, Mika V},
booktitle = {Proc. SPIE},
doi = {10.1117/12.420902},
pages = {102--112},
title = {{Behavior of skin color under varying illumination seen by different cameras at different color spaces}},
url = {http://dx.doi.org/10.1117/12.420902},
volume = {4301},
year = {2001}
}
@misc{local_threshold,
abstract = {Adaptive binarization is an important first step in many document analysis and OCR processes. This paper describes a fast adaptive binarization algorithm that yields the same quality of binarization as the Sauvola method,1 but runs in time close to that of global thresholding methods (like Otsu's method2), independent of the window size. The algorithm combines the statistical constraints of Sauvola's method with integral images.3 Testing on the UW-1 dataset demonstrates a 20-fold speedup compared to the original Sauvola algorithm.},
author = {Shafait, Faisal and Keysers, Daniel and Breuel, Thomas M},
booktitle = {Proc. SPIE},
doi = {10.1117/12.767755},
pages = {681510--681516},
title = {{Efficient implementation of local adaptive thresholding techniques using integral images}},
url = {http://dx.doi.org/10.1117/12.767755},
volume = {6815},
year = {2008}
}
@article{Kumari2015501,
annote = {Eleventh International Conference on Communication Networks, {\{}ICCN{\}} 2015, August 21-23, 2015, Bangalore, India Eleventh International Conference on Data Mining and Warehousing, {\{}ICDMW{\}} 2015, August 21-23, 2015, Bangalore, India Eleventh International Conference on Image and Signal Processing, {\{}ICISP{\}} 2015, August 21-23, 2015, Bangalore, India},
author = {Kumari, Apurva and Sahoo, S K},
doi = {http://dx.doi.org/10.1016/j.procs.2015.06.057},
file = {::},
issn = {1877-0509},
journal = {Procedia Computer Science},
keywords = {Bad weather condition},
pages = {501--507},
title = {{Real Time Visibility Enhancement for Single Image Haze Removal}},
url = {http://www.sciencedirect.com/science/article/pii/S1877050915013812},
volume = {54},
year = {2015}
}
@article{Bai2015244,
annote = {2014 Asia-Pacific International Symposium on Aerospace Technology, {\{}APISAT2014{\}} September 24-26, 2014 Shanghai, China},
author = {Bai, Linting and Wu, Yongwei and Xie, Jianchun and Wen, Pengcheng},
doi = {http://dx.doi.org/10.1016/j.proeng.2014.12.532},
issn = {1877-7058},
journal = {Procedia Engineering},
number = {1},
pages = {244--252},
title = {{Real Time Image Haze Removal on Multi-core {\{}DSP{\}}}},
url = {http://www.sciencedirect.com/science/article/pii/S1877705814036467},
volume = {99},
year = {2015}
}
@inproceedings{Zhou2013,
abstract = {Haze removal from a degraded image which is attenuated by the turbid media in the atmosphere is an important task in image and video processing. From a computer vision perspective, this task is extremely challenging because the haze is dependent on the unknown depth information, and will be under-constrained if only one single haze image is input. If the depth distribution is known, the problem can be well solved. In this paper, we propose a new method to estimate the transmission from which depth distribution can be obtained. It is based on the assumption that the airlight which depends on the distance of objects to the observers, tends to be smooth, and is similar to the Retinex theory. Motivated by this, we use a simple Gaussian filter to convolve the image to obtain the airlight just like we did in Retinex. Experimental results verify the effectiveness of this method.},
author = {Zhou, Jianjun and Zhou, Fugen},
booktitle = {Proceedings - 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation, IMSNA 2013},
doi = {10.1109/IMSNA.2013.6743260},
isbn = {978-1-4799-2716-6},
keywords = {Gaussian filter,Image dehazing,Retinex theory},
pages = {243--247},
title = {{Single image dehazing motivated by Retinex theory}},
year = {2013}
}
@incollection{dplidiar,
address = {Xi'an, China},
author = {Yue, Qinlan and Zheng, Hongjie and Jiang, Chengneng},
booktitle = {Proceedings of the 15th International Conference on Man-Machine-Environment System Engineering},
editor = {Long, Shengzhao and Dhillon, Balbir S},
file = {::},
keywords = {UAV reconnaissance; Dark pixels law; Image dehazin},
pages = {199--206},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Electrical Engineering},
title = {{Dark Pixels Law-Based Image Dehazing in UAV Reconnaissance}},
volume = {356},
year = {2015}
}
@inproceedings{Muckell2010,
abstract = {The massive volumes of trajectory data generated by inex- pensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the qual- ity of the information. We present results froma comprehen- sive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The al- gorithms are compared using several criteria including how well they preserve the spatio-temporal information across numerous real-world datasets, execution times and various error metrics. Such comparisons are useful in identifying the most effective algorithms for various situations. We also provide recommendations for a hybrid algorithm which can leverage the strengths of various algorithms while mitigating their},
author = {Muckell, Jonathan and Hwang, Jeong-Hyon and Lawson, Catherine T. and Ravi, S. S.},
booktitle = {Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '10},
doi = {10.1145/1869790.1869847},
isbn = {9781450304283},
pages = {402},
title = {{Algorithms for compressing GPS trajectory data}},
url = {http://portal.acm.org/citation.cfm?doid=1869790.1869847},
year = {2010}
}
@inproceedings{pang2011improved,
address = {Xi'an, China},
author = {Pang, Jiahao and Oscar, A and Zheng, G},
booktitle = {Proceedings of the APSIPA Annual Summit and Conference (APSIPA ASC)},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Pang, Oscar, Zheng - 2011 - Improved single image dehazing using guided filter.pdf:pdf},
month = {oct},
pages = {1--4},
title = {{Improved single image dehazing using guided filter}},
volume = {1},
year = {2011}
}
@inproceedings{Burger2012,
abstract = {Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms. In this work we attempt to learn this mapping directly with a plain multi layer perceptron (MLP) applied to image patches. While this has been done before, we will show that by training on large image databases we are able to compete with the current state-of-the-art image denoising methods. Furthermore, our approach is easily adapted to less extensively studied types of noise (by merely exchanging the training data), for which we achieve excellent results as well.},
author = {Burger, Harold C. and Schuler, Christian J. and Harmeling, Stefan},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2012.6247952},
isbn = {9781467312264},
issn = {10636919},
pages = {2392--2399},
title = {{Image denoising: Can plain neural networks compete with BM3D?}},
year = {2012}
}
@misc{,
booktitle = {raspberryparatorpes.net},
title = {{No Title}},
url = {https://raspberryparatorpes.net/raspberry-pi-sistemas-operativos/},
year = {2017}
}
@article{Kriesel2005,
abstract = {Originally, this work has been prepared in the framework of a seminar of the University of Bonn in Germany, but it has been and will be extended (after being presented and published online under www.dkriesel.com on 5/27/2005). First and foremost, to provide a comprehensive overview of the subject of neural networks and, second, just to acquire more and more knowledge about LATEX. And who knows – maybe one day this summary will become a real preface!},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Kriesel, David},
doi = {10.1016/0893-6080(94)90051-5},
eprint = {arXiv:1011.1669v3},
isbn = {1476-4679 (Electronic)$\backslash$n1465-7392 (Linking)},
issn = {1432-0711},
journal = {Retrieved August},
pages = {244},
pmid = {20449599},
title = {{A Brief Introduction to Neural Networks}},
url = {http://www.dkriesel.com/en/science/neural{\_}networks},
year = {2005}
}
@article{rojas2004segmentacion,
author = {Rojas, J J B and Guerrero, M L and Acevedo, J C and Vivanco, A P and Serrano, G U},
journal = {Revista Mexicana De Fisica},
number = {6},
pages = {579--587},
title = {{Segmentaci{\{}{\'{o}}{\}}n de im{\{}{\'{a}}{\}}genes de color}},
volume = {50},
year = {2004}
}
@article{Yan2014,
abstract = {Driving in foggy weather is a potentially dangerous activity that has been investigated using various approaches. However, most of the previous research has focused on the driver's response in a particular situation under foggy conditions and lacks a systematic analysis of driving performance, especially speed-related behaviors. This paper presents a hierarchical driving performance assessment method to investigate the effects of foggy conditions on drivers' speed control behaviors. With this method, driving behaviors were tested in three simulated driving scenarios classified into three risk levels: basic speed control while driving along road segments at a low risk level, dynamic speed adjustment in car-following situations at a medium risk level, and emergent speed responses to precrash situations at a high risk level. The driving simulation experiment results indicated that the drivers intended to reduce their speeds in order to lower the driving risk in foggy conditions at all three risk levels. However, due to the limited visibility in fog, the drivers could not observe and respond to impending changes in road geometries in a timely way, which resulted in higher operating speeds than in clear weather conditions. At the medium risk level, the drivers' dynamic speed adjustment behavior was degraded in the fog, with both acceleration and deceleration rates lower than in the clear conditions; therefore, more rear-end collisions happened in the foggy conditions than in the clear conditions. At the high risk level, the experiment results showed that drivers' speed compensation in foggy conditions does not sufficiently reduce their crash-involvement risk, but it can effectively lower the crash severity, as indicated by a significantly lower collision speed. ?? 2014 Elsevier Ltd.},
author = {Yan, Xuedong and Li, Xiaomeng and Liu, Yang and Zhao, Jia},
doi = {10.1016/j.ssci.2014.04.013},
issn = {18791042},
journal = {Safety Science},
keywords = {Crash-involvement risk,Driving simulator,Foggy conditions,Speed control},
pages = {275--287},
pmid = {1531974},
title = {{Effects of foggy conditions on drivers' speed control behaviors at different risk levels}},
volume = {68},
year = {2014}
}
@article{ding2013efficient,
author = {Ding, Meng and Tong, RuoFeng Feng},
doi = {10.1007/s11432-012-4566-y},
file = {::},
issn = {1674-733X},
journal = {Science China Information Sciences},
keywords = {image dehazing,quadtree,soft matting},
number = {9},
pages = {1--9},
publisher = {Springer Berlin Heidelberg},
title = {{Efficient dark channel based image dehazing using quadtrees}},
volume = {56},
year = {2013}
}
@article{YoonWaveLength,
author = {Yoon, Inhye and Jeong, Seokhwa and Jeong, Jaeheon and Seo, Doochun and Paik, Joonki},
doi = {10.3390/s150306633},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Yoon et al. - 2015 - Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images.pdf:pdf},
issn = {1424-8220},
journal = {Sensors},
number = {3},
pages = {6633},
title = {{Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images}},
url = {http://www.mdpi.com/1424-8220/15/3/6633},
volume = {15},
year = {2015}
}
@inproceedings{unnikrishnan2005measures,
address = {Breckenridge, USA},
author = {Unnikrishnan, R and Hebert, M},
booktitle = {Seventh IEEE Workshops on Application of Computer Vision},
doi = {10.1109/ACVMOT.2005.71},
keywords = {computer vision;image segmentation;computer vision},
pages = {394},
title = {{Measures of Similarity}},
volume = {1},
year = {2005}
}
@article{Heckbert1997,
abstract = {Many applications in computer graphics require complex, highly detailed models. However, the level of detail actually necessary may vary considerably. To control processing time, it is often desir- able to use approximations in place of excessively detailedmodels. We have developed a surface simplification algorithmwhich can rapidly produce high quality approximations of polygonal models. The algorithm uses iterative contractions of vertex pairs to simplify models and maintains surface error approximations using quadric matrices. By contracting arbitrary vertex pairs (not just edges), our algorithm is able to join unconnected regions of models. This can facilitate much better approximations, both visually and with re- spect to geometric error. In order to allow topological joining, our system also supports non-manifold surface models.},
author = {Heckbert, Paul S. and Garland, Michael},
doi = {10.1145/258734.258849},
isbn = {0897918967},
journal = {SIGGRAPH},
keywords = {level of detail,multiresolution modeling,non-manifold,pair contraction,surface simplification},
number = {May},
title = {{Survey of Polygonal Surface Simplification Algorithms}},
year = {1997}
}
@article{Gao,
author = {Gao, Yuanyuan and Hu, Hai-Miao and Wang, Shuhang and Li, Bo},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Gao et al. - 2014 - A fast image dehazing algorithm based on negative correction.pdf:pdf},
journal = {Signal Processing},
number = {1},
pages = {380--398},
title = {{A fast image dehazing algorithm based on negative correction}},
volume = {103},
year = {2014}
}
@article{Xie2016,
abstract = {Haze is one of the major factors that degrade outdoor images, and dehazing becomes an important issue in many applications. In order to address the problems of being unsmooth and the absence of neighbor information for the transmission estimation under Dark Channel Prior (DCP) framework, we proposed a new improved method using Kernel Regression Model (KRM) on local neighbor data. Firstly, the initial transmission in atmospheric light model is estimated by DCP. Secondly, the transmission is refined according to KRM. Experimental results on synthetic and real images show that our method can address this problem and has better dehazing results than several state-of-the-art methods.},
author = {Xie, Cong-Hua and Qiao, Wei-Wei and Liu, Zhe and Ying, Wen-Hao},
doi = {10.1007/s11760-016-1013-3},
issn = {1863-1711},
journal = {Signal, Image and Video Processing},
pages = {1--8},
title = {{Single image dehazing using kernel regression model and dark channel prior}},
url = {http://dx.doi.org/10.1007/s11760-016-1013-3},
year = {2016}
}
@article{Dosselmann2011,
author = {Dosselmann, Richard and Yang, XueDong},
doi = {10.1007/s11760-009-0144-1},
issn = {1863-1703},
journal = {Signal, Image and Video Processing},
keywords = {Structural similarity index; SSIM; Mean squared er},
number = {1},
pages = {81--91},
publisher = {Springer-Verlag},
title = {{A comprehensive assessment of the structural similarity index}},
volume = {5},
year = {2011}
}
@article{Karas2011,
author = {Karas, Pavel},
doi = {10.4230/OASIcs.MEMICS.2010.54},
file = {::},
journal = {Sixth Doctoral Workshop on Mathematical and Engineering Methods in Computer Science},
pages = {54--61},
title = {{Efficient Computation of Morphological Greyscale Reconstruction}},
year = {2011}
}
@article{Bhattacharjee2010,
author = {Bhattacharjee, Debotosh and Basu, Dipak K. and Nasipuri, Mita and Kundu, Mohantapash},
doi = {10.1007/s00500-009-0426-0},
issn = {14327643},
journal = {Soft Computing},
keywords = {Artificial neural network,Face recognition,Feature extraction,Fuzzy multilayer perceptron,Gabor wavelets,Multilayer perceptron},
number = {6},
pages = {559--570},
title = {{Human face recognition using fuzzy multilayer perceptron}},
volume = {14},
year = {2010}
}
@article{Pavlidis77,
author = {Pavlidis, T},
journal = {Springer Series in Electrophysics, Berlin: Springer, 1977},
pages = {244},
title = {{Structural pattern recognition}},
volume = {1},
year = {1977}
}
@article{Visvalingam1993,
abstract = {This paper presents a new approach to line generalisation which uses the concept of 'effective area' for progressive simplification of a line by point elimination. Two coast- lines are used to compare the performance of this, with that of the widely used Douglas-Peucker, algorithm. The results from the area-based algorithm compare favourably with manual generalisation of the same lines. It is capable of achieving both imperceptible minimal simplifications and caricatural generalisations. By careful selection of cut- off values, it is possible to use the same algorithm for scale-dependent and scale-independent generalisations. More importantly, it offers scope for modelling carto- graphic lines as consisting of features within features so that their geometric manipulation may be modified by application- and/or user-defined rules and weights. The paper examines the merits and limitations of the algorithm and the opportunities it offers for further research and progress in the field of line generalisation.},
author = {Visvalingam, M. and Whyatt, J. D.},
doi = {10.1179/000870493786962263},
isbn = {0008-7041},
issn = {00087041},
journal = {The Cartographic Journal},
number = {1},
pages = {46--51},
title = {{Line generalisation by repeated elimination of points}},
volume = {30},
year = {1993}
}
@inproceedings{Berman_2016_CVPR,
author = {Berman, Dana and Treibitz, Tali and Avidan, Shai},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Berman, treibitz, Avidan - 2016 - Non-Local Image Dehazing.pdf:pdf},
month = {jun},
title = {{Non-Local Image Dehazing}},
year = {2016}
}
@article{raey,
author = {Ling, Zhigang and Li, Shutao and Wang, Yaonan and Shen, He and Lu, Xiao},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ling et al. - 2015 - Adaptive transmission compensation via human visual system for efficient single image dehazing.pdf:pdf},
issn = {0178-2789},
journal = {The Visual Computer},
keywords = {Single image dehazing; Human visual system; Just-n},
pages = {1--10},
publisher = {Springer Berlin Heidelberg},
title = {{Adaptive transmission compensation via human visual system for efficient single image dehazing}},
year = {2015}
}
@techreport{molina1998introduccion,
author = {Molina, R},
booktitle = {Universidad de Granada. Granada},
institution = {Universidad de Granada},
number = {30},
title = {{Introducci{\{}{\'{o}}{\}}n al procesamiento y an{\{}{\'{a}}{\}}lisis de im{\{}{\'{a}}{\}}genes digitales}},
year = {1998}
}
@inproceedings{hu2009haze,
address = {Shanghai,China},
author = {Hu, Jianbo and Chen, Wei and Li, Xiaoyu and He, Xingyuan},
booktitle = {Urban Remote Sensing Event, 2009 Joint},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hu et al. - 2009 - A haze removal module for mutlispectral satellite imagery.pdf:pdf},
month = {may},
pages = {1--4},
title = {{A haze removal module for mutlispectral satellite imagery}},
volume = {1},
year = {2009}
}
@article{cheng1995mean,
author = {Cheng, Y},
journal = {vIEEE Conference onIEEE Conference on},
number = {8},
pages = {790--799},
publisher = {IEEE},
title = {{Mean shift, mode seeking, and clustering}},
volume = {17},
year = {1995}
}
@article{Narasimhan2004,
abstract = {Current vision systems are designed to perform in clear weather. Needless to say, in any outdoor application, there is no escape from bad weather. Ultimately, computer vision systems must include mechanisms that enable them to function (even if somewhat less reliably) in the presence of haze, fog, rain, hail and snow. We begin by studying the visual manifestations of different weather conditions. For this, we draw on what is already known about atmospheric optics, and identify effects caused by bad weather that can be turned to our advantage; we are not only interested in what bad weather does to vision but also what it can do for vision.},
author = {Narasimhan, S G},
file = {::},
journal = {Weather},
pages = {223},
title = {{Models and algorithms for vision through the atmosphere}},
url = {http://portal.acm.org/citation.cfm?id=997554},
year = {2004}
}
@article{gil2004deteccion,
author = {Gil, P and Torres, F and Ortiz, F G},
journal = {XXV Jornadas de Autom{\{}{\'{a}}{\}}tica},
pages = {84--688},
title = {{Detecci{\{}{\'{o}}{\}}n de objetos por segmentaci{\{}{\'{o}}{\}}n multinivel combinada de espacios de color}},
volume = {25},
year = {2004}
}
@book{banger2013opencl,
author = {Banger, Ravishekhar and Bhattacharyya, Koushik},
publisher = {Packt Publishing Ltd},
title = {{OpenCL programming by example}},
year = {2013}
}
@techreport{white1991data,
author = {White, F E},
institution = {DTIC Document},
title = {{Data fusion lexicon}},
year = {1991}
}
@book{espanola2011real,
author = {Espa{\~{n}}ola, R A and RAE},
publisher = {Real Academia Espa{\{}{\~{n}}{\}}ola},
title = {{Real Academia Espa{\{}{\~{n}}{\}}ola}},
year = {2011}
}
@article{Tema,
author = {Tema, P I D},
file = {::},
title = {{Tema 5 : Morfolog{\'{i}}a Segunda parte Morfolog{\'{i}}a}}
}
@book{2040007,
author = {Bovik, Alan C},
publisher = {Academic press},
title = {{Handbook of Image and Video Processing}},
year = {2000}
}
@book{sonka1999image,
author = {Sonka, M and Hlavac, V and Boyle, R},
publisher = {International Thomson},
title = {{Image processing, analysis, and machine vision second edition}},
year = {1999}
}
@misc{TheMeteorologicalOffice1982,
author = {{The Meteorological Office}},
file = {::},
title = {{Observer's Handbook (M.O.1028), 4th editition}},
year = {1982}
}
@book{moles1981image,
author = {Moles, A A and Rohmer, E},
editor = {Casterman},
publisher = {Casterman},
title = {{L' image, communication fonctionnelle}},
volume = {29},
year = {1981}
}
@article{,
file = {::},
isbn = {9781848212152},
pages = {42},
title = {{Mathematical Morphology}}
}
@misc{barrientos2012fundamentos,
author = {{Barrientos Cruz}, Antonio},
publisher = {Espa{\{}{\~{n}}{\}}a},
title = {{Fundamentos de rob{\'{o}}tica}},
year = {2012}
}
@inproceedings{Baruwa2013,
abstract = {This paper outlines the development of a robust, automated and real-time horizon detection and tracking system for the purpose of distance estimation of the sea surface. The distance information facilitates visual monitoring of the sea surface in remote sensing or monitoring applications. The specific application of the system in marine mammal mitigation is used to trial and demonstrate the effectiveness of the system. Results from the trials are presented and shows the effectiveness of the system. },
author = {Baruwa, Abdulquadir L and Evans, Adrian N and Wyatt, Roy},
pages = {889214--889217},
title = {{The development of a remote sensing system with real-time automated horizon tracking for distance estimation at sea}},
url = {http://dx.doi.org/10.1117/12.2028786},
volume = {8892},
year = {2013}
}
@article{Couturier2013,
author = {Couturier, Raphael},
keywords = {Book,CUDA,Computer science,Factorization,Fluid simulation,Image processing,Mathematics,Physics,Tutorial,nVidia,paper},
publisher = {hgpu.org},
title = {{Designing Scientific Applications on GPUs}},
year = {2013}
}
@article{Tcheslavski2010,
author = {Tcheslavski, Gleb V},
file = {::},
pages = {1--19},
title = {{Morphological Image Processing : Gray ‐ scale morphology}},
year = {2010}
}
@article{Khoury2015,
author = {Khoury, Jessica El and Thomas, Jean-baptiste and Mansouri, Alamin},
file = {::},
title = {{HAZE AND CONVERGENCE MODELS : EXPERIMENTAL COMPARISON HAZE AND CONVERGENCE MODELS :}},
year = {2015}
}
@book{beck2014human,
author = {Beck, Jacob and Hope, Barbara and Rosenfeld, Azriel},
publisher = {Academic Press},
title = {{Human and machine vision}},
volume = {8},
year = {2014}
}
@book{shapiro2001computer,
author = {Shapiro, L G and Stockman, G C},
publisher = {Prentice Hall},
title = {{Computer Vision}},
year = {2005}
}
@book{groover1997fundamentos,
author = {Groover, Mikell P},
publisher = {Pearson Educaci{\{}{\'{o}}{\}}n},
title = {{Fundamentos de manufactura moderna: materiales, procesos y sistemas}},
year = {1997}
}
@article{,
file = {::},
pages = {1--36},
title = {{Filtrado no lineal : morfolog{\'{i}}a}}
}
@book{ballardcomputer,
author = {Ballard, D H and Brown, C M},
isbn = {9780131653160},
publisher = {Prentice-Hall},
title = {{Computer vision}},
year = {1982}
}
@article{Feitosa,
author = {Feitosa, Raul Queiroz and Costa, Gilson A O P},
file = {::},
title = {{M h l i lI}}
}
@book{Gonzalez:2006:DIP:1076432,
address = {Upper Saddle River, NJ, USA},
author = {Gonzalez, Rafael C and Woods, Richard E},
isbn = {0-201-18075-8},
publisher = {Prentice-Hall, Inc.},
title = {{Digital Image Processing (3rd Edition)}},
year = {2006}
}
@book{Theodoridis:2010:IPR:1841600,
author = {Theodoridis, Sergios and Pikrakis, Aggelos and Koutroumbas, Konstantinos and Cavouras, Dionisis},
isbn = {0123744865, 9780123744869},
publisher = {Academic Press},
title = {{Introduction to Pattern Recognition: A Matlab Approach}},
year = {2010}
}
@book{soille2013morphological,
author = {Soille, Pierre},
publisher = {Springer Science {\&} Business Media},
title = {{Morphological image analysis: principles and applications}},
year = {2013}
}
@book{Nixon:2002:FEI,
address = {Oxford},
author = {Nixon, Mark S and Aguado, Alberto S},
isbn = {0-7506-5078-8},
keywords = {Matlab},
pages = {xii + 350},
publisher = {Newnes},
title = {{Feature extraction and image processing}},
year = {2002}
}
@book{cvaars,
author = {Szeliski, Richard},
publisher = {Springer},
title = {{Computer Vision: Algorithms and Applications}},
year = {2011}
}
@phdthesis{thesisCr,
author = {Thurow, Christian T},
file = {:C$\backslash$:/Users/screa/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Thurow - 2011 - Real-Time Image Dehazing.pdf:pdf},
school = {Technische Universit�t Berlin},
title = {{Real-Time Image Dehazing}},
year = {2011}
}
@book{koschmieder1925theorie,
author = {Koschmieder, H},
publisher = {Keim {\&} Nemnich},
series = {Beitr{\{}{\"{a}}{\}}ge zur Physik der freien Atmosph{\{}{\"{a}}{\}}re},
title = {{Theorie der horizontalen Sichtweite: Kontrast und Sichtweite}},
year = {1925}
}
@misc{,
file = {::},
title = {{A simulation of the effect of haze in headlamp.pdf}}
}
@book{young1995fundamentals,
author = {Young, Ian T and Gerbrands, Jan J and {Van Vliet}, Lucas J},
publisher = {Delft University of Technology Delft, The Netherlands},
title = {{Fundamentals of image processing}},
year = {1998}
}
@article{,
file = {::},
title = {{Transformaciones Geod{\'{e}}sicas}}
}
@article{Liu2014,
author = {Liu, Yu and Liu, Shuping},
file = {::},
isbn = {9781479957514},
number = {1},
pages = {3449--3453},
title = {{Department of Automation , University of Science and Technology of China , Hefei , China Institute of Intelligent Machines , Chinese Academy of Sciences , Hefei , China}},
year = {2014}
}
@book{hall2004mathematical,
author = {Hall, D L and McMullen, S A H},
publisher = {Artech House Publishers},
title = {{Mathematical techniques in multisensor data fusion}},
year = {2004}
}
@article{Serra,
author = {Serra, J},
file = {::},
pages = {1--36},
title = {{Cap{\'{i}}tulo Cap{\'{i}}tulo V : V : Geodesia Geodesia binaria binaria y y Conectividad Conectividad Conectividad topol{\'{o}}gica topol{\'{o}}gica ( ( recordatorio}}
}
@inproceedings{Fang2013,
abstract = {Images are usually degraded by haze and fog due to atmospheric absorption and scattering. Especially for remote sensing, degraded images which suffer from haze will highly probably lose detailed information. Nowadays researches on haze removal mainly focus on visible images. However, as infrared imaging is more and more widely used in many fields, a comprehensive study of the effects of fog on infrared images is urgent. In this paper, we firstly introduce that the obvious attenuation effects of fog on infrared images by analyzing the mid-infrared images captured under different weather conditions. Therefore, we propose a unified dehazing approach for infrared images. The proposed approach mainly includes three steps. The first step is to obtain the local transmission of the original hazed images by using the statistical prior knowledge known as “dark channel” which is similar to visible images. With local transmission, the refined transmission map is estimated in a soft matting framework. Due to atmospheric scattering model, dehazed infrared images can be recovered finally. From experimental results, it is quantitatively demonstrated that the proposed approach can significantly improve the quality of hazed images by using principle component analysis technique. },
author = {Fang, Tao and Cao, Zhiguo and Yan, Ruicheng},
pages = {89170V--8917--7},
title = {{A unified dehazing approach for infrared images}},
url = {http://dx.doi.org/10.1117/12.2031255},
volume = {8917},
year = {2013}
}
@misc{gallant,
annote = {Consultado el 15 de Mayo del 2012},
author = {Http//:www.gallantlab.org/},
howpublished = {Web Page},
title = {{The Gallant Lab at UC Berkeley}},
year = {2011}
}
@book{zhang2006advances,
author = {Zhang, Yu-Jin},
publisher = {IGI Global},
title = {{Advances in image and video segmentation}},
year = {2006}
}
@book{bb2040,
author = {Efford, N},
publisher = {Addison Wesley},
title = {{Digital Image Processing: {\{}A{\}} Practical Introduction Using Java}},
year = {2000}
}
@book{nevatia1982machine,
author = {Nevatia, R},
publisher = {PrenticPrentice Inc., Englewood Cliffs, NJ 07632, 1982, 209},
title = {{Machine perception.}},
year = {1982}
}
@book{shih2009image,
author = {Shih, F Y},
publisher = {Wiley-IEEE Press},
title = {{Image processing and pattern recognition: fundamentals and techniques}},
year = {2009}
}
@article{panagiotakis2011natural,
author = {Panagiotakis, C and Grinias, I and Tziritas, G},
number = {8},
pages = {2276},
title = {{Natural Image Segmentation based on Tree Equipartition, Bayesian Flooding and Region Merging.}},
volume = {20},
year = {2011}
}
@article{Gibson2012,
abstract = {This paper makes an investigation of the dehazing effects on image and video coding for surveillance systems. The goal is to achieve good dehazed images and videos at the receiver while sustaining low bitrates (using compression) in the transmission pipeline. At first, this paper proposes a novel method for single-image dehazing, which is used for the investigation. It operates at a faster speed than current methods and can avoid halo effects by using the median operation. We then consider the dehazing effects in compression by investigating the coding artifacts and motion estimation in cases of applying any dehazing method before or after compression. We conclude that better dehazing performance with fewer artifacts and better coding efficiency is achieved when the dehazing is applied before compression. Simulations for Joint Photographers Expert Group images in addition to subjective and objective tests with H.264 compressed sequences validate our conclusion.},
author = {Gibson, Kristofor B and V{\~{o}}, Dung T and Nguyen, Truong Q},
doi = {10.1109/TIP.2011.2166968},
file = {::},
issn = {1941-0042},
number = {2},
pages = {662--673},
pmid = {21896391},
title = {{An investigation of dehazing effects on image and video coding.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21896391},
volume = {21},
year = {2012}
}
@article{,
file = {::},
title = {{Operaciones morfol{\'{o}}gicas b{\'{a}}sicas}}
}
@book{rauber2013parallel,
author = {Rauber, Thomas and R{\"{u}}nger, Gudula},
publisher = {Springer Science {\&} Business Media},
title = {{Parallel programming: For multicore and cluster systems}},
year = {2013}
}
@article{Serra2000a,
author = {Serra, J},
file = {::},
pages = {1--26},
title = {{Cap{\'{i}}tulo IV Filtrado Morfol{\'{o}}gico Definici{\'{o}}n Definici{\'{o}}n de de filtro filtro morfol{\'{o}}gico morfol{\'{o}}gico ( I ) ( I )}},
year = {2000}
}
@phdthesis{fdvnif,
author = {Karlsson, Jonas},
file = {::},
school = {Malardalen University},
title = {{FPGA-Accelerated Dehazing by Visible and Near-infrared Image Fusion}},
year = {2015}
}
@article{,
file = {::},
pages = {1--24},
title = {{M{\'{e}}todos Avanzados para An{\'{a}}lisis y Representaci{\'{o}}n Contenido}},
year = {2012}
}
@phdthesis{roberts1963machine,
author = {Roberts, Lawrence Gilman},
school = {Massachusetts Institute of Technology},
title = {{Machine perception of three-dimensional soups}},
year = {1963}
}
@book{cai2015gpu,
author = {Cai, Yiyu and See, Simon},
publisher = {Springer},
title = {{GPU Computing and Applications}},
year = {2015}
}
@book{jahne1999handbook,
author = {Jahne, B and Haussecker, H and Geissler, P},
publisher = {Academic Press, Inc.},
title = {{Handbook of Computer Vision and Applications: Volume 2: From Images to Features}},
year = {1999}
}
@book{engelberger2012robotics,
author = {Engelberger, Joseph F},
publisher = {Springer Science {\&} Business Media},
title = {{Robotics in practice: management and applications of industrial robots}},
year = {2012}
}
@article{Autiere,
author = {Auti{\`{e}}re, N Icolas H and Arel, J E A N Hilippe T and Ubert, D Idier A and Umont, {\'{E}} R I C D},
file = {::},
keywords = {advanced driver assistance system,blind assessment,contrast enhancement,contrast restoration,edges segmentation,lip model,tone-mapping,visibility level},
number = {1},
pages = {1--9},
title = {{Blind Contrast Enhancement Assessment By Gradient Ratioing At Visible Edges}}
}
@article{Serra2000,
author = {Serra, J},
file = {::},
title = {{Cap{\'{i}}tulo Cap{\'{i}}tulo VI : VI : Geodesia Geodesia Num{\'{e}}rica Num{\'{e}}rica Paso Paso a a funciones funciones num{\'{e}}ricas num{\'{e}}ricas}},
year = {2000}
}
@inproceedings{Jiang2016,
abstract = {For unmanned aerial vehicle(UAV) images, the sensor can not get high quality images due to fog and haze weather. To solve this problem, An improved dehazing algorithm of aerial high-definition image is proposed. Based on the model of dark channel prior, the new algorithm firstly extracts the edges from crude estimated transmission map and expands the extracted edges. Then according to the expended edges, the algorithm sets a threshold value to divide the crude estimated transmission map into different areas and makes different guided filter on the different areas compute the optimized transmission map. The experimental results demonstrate that the performance of the proposed algorithm is substantially the same as the one based on dark channel prior and guided filter. The average computation time of the new algorithm is around 40{\%} of the one as well as the detection ability of UAV image is improved effectively in fog and haze weather.},
author = {Jiang, Wentao and Ji, Ming and Huang, Xiying and Wang, Chao and Yang, Yizhou and Li, Tao and Wang, Jiaoying and Zhang, Ying},
pages = {97962T--9796--9},
title = {{An improved dehazing algorithm of aerial high-definition image}},
url = {http://dx.doi.org/10.1117/12.2229845},
volume = {9796},
year = {2016}
}
@phdthesis{ssc,
author = {Salazar-Colores, Sebasti�n},
school = {Universidad de Guanajuato},
title = {{Optimizaci�n computacional para el dehazing en im�genes}},
year = {2015}
}
@misc{,
file = {::},
title = {{Motion Analysis.pdf}}
}
@phdthesis{ThesisStudy,
author = {Mao, Jun},
file = {::},
school = {Muroran Institute of Technology},
title = {{Study on Image Dehazing with the Self-Adjustment of the Haze Degree}},
year = {2015}
}
@book{fischler1987intelligence,
author = {Fischler, M A and Firschein, O},
publisher = {Addison-Wesley Pub. Co. Inc., Reading, MA},
title = {{Intelligence: the eye, the brain, and the computer}},
year = {1987}
}
@book{theodoridis2008pattern,
author = {Theodoridis, S and Koutroumbas, K},
publisher = {Academic press},
title = {{Pattern recognition}},
year = {2008}
}
@book{acharya2005image,
author = {Acharya, T and Ray, A K},
publisher = {Wiley-Interscience},
title = {{Image processing: principles and applications}},
year = {2005}
}

@Comment{jabref-meta: databaseType:bibtex;}
