Abstract

The rain removal from an image in the rainy season is also a required task to identify the object in it. It is a challenging problem and has been recently investigate extensively. In this paper the entropy maximization and background estimation based method is used for the rain removal. This method is based on single-image rain removal framework. The raindrops are greatly differing from the background, as the intensity of rain drops is higher the background. The entropy maximization is very much suitable for the rain removal. Experimental results express the efficacy of the rain removal by proposed algorithm is better than the method based on saturation and visibility features.

References

• P. C. Barnum, S. Narasimhan, and T. Kanade, “Analysis of rain and snow in frequency space,” Int. J. Comput. Vis., vol. 86, no. 2/3, pp. 256–274, Jan. 2010.
• K. Garg and S. K. Nayar, “Detection and removal of rain from videos,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2004, vol. 1, pp. 528–535.
• K. Garg and S. K. Nayar, “When does a camera see rain?,” in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2005, vol. 2, pp. 1067–1074.
• K. Garg and S. K. Nayar, “Vision and rain,” Int. J. Comput. Vis., vol. 75, no. 1, pp. 3–27, Oct. 2007.
• X. Zhang, H. Li, Y. Qi, W. K. Leow, and T. K. Ng, “Rain removal in video by combining temporal and chromatic properties,” in Proc. IEEE Int. Conf. Multimedia Expo., Toronto, ON, Canada, Jul. 2006, pp. 461–464.
• N. Brewer and N. Liu, “Using the shape characteristics of rain to identify and remove rain from video,” Lecture Notes Comput. Sci., vol. 5342/2008, pp. 451–458, 2008.
• J. Bossu, N. Hautière, and J. P. Tarel, “Rain or snow detection in image sequences through use of a histogram of orientation of streaks,” Int. J. Comput. Vis., vol. 93, no. 3, pp. 348–367, Jul. 2011.
• M. Roser and A. Geiger, “Video-based raindrop detection for improved image registration,” in IEEE Int. Conf. Comput. Vis.Workshops, Kyoto, Sep. 2009, pp. 570–577.
• J. C. Halimeh and M. Roser, “Raindrop detection on car windshields using geometric–photometric environment construction and intensitybased correlation,” in Proc. IEEE Intell. Veh. Symp., Xi’an, China, Jun. 2009, pp. 610–615.
• N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., San Diego, CA, Jun. 2005, vol. 1, pp. 886–893.
• J. M. Fadili, J. L. Starck, J. Bobin, and Y. Moudden, “Image decomposition and separation using sparse representations: An overview,” Proc.IEEE, vol. 98, no. 6, pp. 983–994, Jun. 2010.
• J. L. Starck, M. Elad, and D. L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE Trans. Image Process., vol. 14, no. 10, pp. 1570–1582, Oct. 2005.
• S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397–3415, Dec. 1993.
• D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006.
• A. M. Bruckstein, D. L. Donoho, and M. Elad, “From sparse solutions of systems of equations to sparse modeling of signals and images,” SIAM Rev., vol. 51, no. 1, pp. 34–81, Feb. 2009.
• Li-Wei Kang, Chia-Wen Lin and Yu-Hsiang Fu “Automatic single-image-based rain streaks removal via image decomposition IEEE Trans. on Image Process., vol. 21, no. 4, April 2012.
• D. Sharma, Y. Kurmi, and V. Chaurasia, “Formation of super- resolution image: a review,” Int. Jour. of Emerging Tech. and Adv. Engg., Apr. 2014, vol. 4, no. 4, pp. 218-221.
• Y. Kurmi and V. Chaurasia, “An image fusion approach based on adaptive fuzzy logic model with local level processing,” Int. Jour. of Comp. Appl., Aug. 2015, vol. 124, no.1, pp. 39-42.
• S. Tiwari, K. Chauhan, and Y. Kurmi “Shadow detection and compensation in aerial images using MATLAB,” Int. Jour. of Comp. Appl., June 2015, vol. 119, no.20, pp. 5-9.
• Y. Kurmi and V. Chaurasia, “Performance of haze removal filter for hazy and noisy images,” Int. Jour. of Sci. Engg. and Tech., Apr. 2014, vol. 3 no. 4, pp. 437-439.
• M. K. Patle, B. Chourasia, and Y. Kurmi, "High dynamic range image analysis through various tone mapping techniques," Int. Jour. of Comp. Appl., vol.153, no. 11, Nov. 2016 pp. 14-17.
• A. Kumar, B. Chourasia, and Y. Kurmi, "Image defogging by multiscale depth fusion and hybrid scattering model," International Journal of Computer Applications (0975 – 8887), vol. 155, no 11, Dec. 2016, pp. 34-38.
• Soo-Chang Pei, Yu-Tai Tsai, and Chen-Yu Lee “Removing rain and snow in a single image using saturation and visibility features, Graduate Inst. of Comm. Engg., Nat. Taiwan University, Taipei, Taiwan.
• Google Goggles [Online]. Available: http://www.google.com/mobile/goggles/
• Y. Niu, X. Wu, and G. Shi, "Image enhancement by entropy maximization and quantization resolution up-conversion," IEEE Trans. on Image Process., vol. 25, no. 10, Oct. 2016 4815
• G. T. Herman et al., “Maximum a posteriori image reconstruction from projections,” in image models (and their speech model cousins), S. Levinson and L. A. Shepp, Eds. New York, NY: Springer-Verlag, 1996.
• A. Mishra, B. Chaurasia and Y. Kurmi, "Comparative Analysis of Single Image Shadow Detection and Removal in Aerial Images," International Journal of Advanced Engineering and Management, Vol. 2, No. 4, pp. 86-89, 2017
• Y. Cao, P. P. B. Eggermont, and S. Terebey, "Cross burg entropy maximization and its application to ringing suppression in image reconstruction," IEEE Trans. on Image Process., vol. 8, no. 2, Feb. 1999, pp.286-292.