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Analysis of Prior Based Fog Removal Methods

manju ., Neelam Turk


 Fog is defined as the weather condition in which visibility becomes less than 1000 m. Fog is caused due to plenty of reasons like bad weather, pollution, drizzle etc. Images degraded by fog have poor contrast and colour allegiance. The efficiency of image-based applications highly degrades due to foggy input images. So fog removal of images is a necessary task in computer vision applications and computational photography. This paper highlights the literature survey of prior based fog removal techniques such as Dark Channel Prior (DCP), Improved Dark Channel Prior (IDCP) and Colour Attenuation Prior (CAP).



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K. He, J. Sun, and X. Tang. Single image haze removal using dark channel prior. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09), pages 1 956-1963, 2009.

SK Nayar, SG Narasimhan, Vision in bad weather, in Proceedings of the 7th IEEE

International Conference on Computer Vision (ICCV, Kerkyra, 1999), pp. 820–827

YY Schechnner, SG Narasimhan, SK Nayar, Polarization-based vision through haze. Appl. Optics 42(3), 511–525 (2003)

SG Narasimhan, SK Nayar, Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)

S Shwartz, E Namer, YY Schechner, Blind haze separation, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (CVPR, Anchorage, 2006), pp. 1984–1991

RT Tan, Visibility in bad weather from a single image, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR, Anchorage, 2008), pp. 1–8

R Fattal, Single image dehazing. ACM Trans. Graph. 72(3), 72:1-72:9 (2008)

N Hautière, JP Tarel, D Aubert, E Dumont, Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis & Stereology Journal. 27(2), 87–95 (2008)

AK Tripathi, S Mukhopadhyay, Removal of fog from images: a review. IETE Tech. Rev. 29(2), 148–156 (2012)

Z Qingsong, Y Shuai, X Yaoqin, An improved single image haze removal algorithm based on dark channel prior and histogram specification, in Proceedings of 3rd International Conference on Multimedia Technology (ICMT, Atlantis Press, Guangzhou, 2013), pp. 279–292

Y. Xiong and H. Yan, ―Improved Single Image Dehazing using Dark Channel Prior‖, Journal of Computational Information Systems, vol. 9, (2013), pp. 5743-5750.

Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar. Instant dehazing of images using polarization. CVPR, 1:325, 2001.

S. Shwartz, E. Namer, and Y. Y. Schechner. Blind haze separation.CVPR, 2:1984–1991, 2006.

Y. Wang and B. Wu, ―Improved Single Image Dehazing using Dark Channel Prior‖, Intelligent Computing and Intelligent Systems , in IEEE International Conference on ,vol. 2, (2010), pp.789-792.

K. He, J. Sun, and X. Tang, ―Guide image Filtering‖, (2010), pp. 1-14.

S. Yang, Q. Zhu, J. Wang, D. Wu and Y. Xie, ―An Improved Single Image Haze Removal Algorithm Based on Dark Channel Prior and Histogram Specification‖, Proc. 3rd International Conf. On Multimedia Technology, Atlantis Press, (2013), pp. 279-292

A. K. Tripathi and Sudipta Mukhopadhay, Single Image Fog Removal using Anisotropic Diffusion‖, IET Image Processing, vol. 6, no. 7, (2012), pp. 966-975.

A. J. Preetham, P. Shirley, and B. Smits. A practical analytic model for daylight. In SIGGRAPH, pages 91–100, 1999.

S. G. Narasimhan and S. K. Nayar. Chromatic framework for vision in bad weather. CVPR, pages 598–605, 2000.

S. G. Narasimhan and S. K. Nayar. Vision and the atmosphere. IJCV, 48:233–254, 2002.

Jehoiada Jackson', Oluwasanmi Ariyo', Kingsley Acheampong', Maxwell Boakye2, Enoch Frimpong2, Eric Ashalley3, Yunbo Rao'*, Hybrid Single Image Dehazing with Bright Channel and Dark Channel Priors,in 2nd International Conference on Image, Vision and Computing(2017)


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