IJBTT

Natural Image and Video Decomposition with Applications to Single Image Denoising

	International Journal of Computer Trends and Technology (IJCTT)
	© 2017 by IJCTT Journal
	Volume-48 Number-3
	Year of Publication : 2017
	Authors : Deepthi A J, Arun Pradeep
	DOI :  10.14445/22312803/IJCTT-V48P128

Deepthi A J, Arun Pradeep "Natural Image and Video Decomposition with Applications to Single Image Denoising". International Journal of Computer Trends and Technology (IJCTT) V48(3):148-159, June 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Rain, fog and Gaussian noise removal from an image and video is a challenging problem and has been recently investigated extensively. In this paper, present a self-learning based image and video decomposition framework. Based on sparse representation, this method first learns an over-complete dictionary from the high spatial frequency parts of the input image for reconstruction purposes. An unsupervised clustering on the observed dictionary atoms has been performed in this work. And their corresponding reconstructed image versions via, affinity propagation, which allows to identify image-dependent components with similar context information. While applying this method for the applications of image and video denoising, it was able to automatically determine the undesirable patterns like rain streaks, Fog or Gaussian noise. From the derived image components directly from the input image or video, so that the task of single-image denoising can be addressed. DWT is used for better performance. Fog will degrade the quality of the preview image by reducing the saturation and contrast.. The objective of this method is to enhance the visibility, saturation, contrast and reduce the noise in a foggy image. Here single frame is used for enhancing foggy images using multi-level transmission map. The method is fast and free from noise. Comparison with the existing method shows that this method provides better processing time and quality. Experiments were conducted based on: single-image and video denoising with Gaussian, Fog and rain noises. The empirical results confirm the effectiveness and robustness of this approach, which is proved to outperform state-of-the-art image denoising algorithms.

References
[1] 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.
[2] M. Aharon, M. Elad, and A. M. Bruckstein, “The K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311–4322, Nov. 2006.
[3] J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. Image Process., vol. 17, no. 1, pp. 53–69, Jan. 2008.
[4] L.-W. Kang, C.-W. Lin, and Y.-H. Fu, “Automatic single-image-based rain streaks removal via image decomposition,” IEEE Trans. Image Process., vol. 21, no. 4, pp. 1742–1755, Apr. 2012.
[5] D.-A. Huang, L.-W. Kang, M.-C. Yang, C.-W. Lin, and Y.-C. F.Wang, “Context-aware single image rain removal,” in Proc. IEEE Int. Conf. Multimedia and Expo, Melbourne, Australia, Jul. 2012, pp. 164–169.
[6] J. Bobin, J. L. Starck, J. M. Fadili, Y. Moudden, and D. L. Donoho, “Morphological component analysis: an adaptive thresholding strategy,” IEEE Trans. Image Process., vol. 16, no. 11, pp. 2675–2681, Nov. 2007.
[7] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” inProc. IEEE Int. Conf. Comput.Vis., Bombay, India, Jan. 1998, pp. 839–846.
[8] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3d transform-domain collaborative filtering,” IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080–2095, Aug. 2007.
[9] J.Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res., vol. 11, pp. 19–60, 2010.
[10] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., San Diego, CA, USA, Jun. 2005, vol. 1, pp. 886–893.
[11] 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.
[12] K. Garg and S. K. Nayar, “Vision and rain,” Int. J. Comput. Vis., vol. 75, no. 1, pp. 3–27, 2007.
[13] 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.
[14] A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model. Simulat., vol. 4, no. 2, pp. 490–530, 2005.
[15] J. Mairal, F. Bach, and J. Ponce, “Task-driven dictionary learning,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 791–804, Apr. 2012.
[16] 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.
[17] T.Blu and F. Luisier, “The sure-let approach to image denoising,” IEEE Trans. Image Process., vol. 16, no. 11, pp. 2778–2786, Nov. 2007.
[18] J. Sahu, “Design a New Methodology for Removing Fog from the Image”, International Journal of Advanced Computer Research, Vol.12 Number-4 Issue-7, pp. 62-65, 2012.
[19] R. Fattal, “Single image dehazing”, SIGGRAPH, 2008.
[20] K. He, J. Sun, & X.Tan, “Single image haze removal using dark channel prior”, IEEE Transaction on pattern Analysis and Machine Intelligence, Vol. 33, No. 12, 2011.
[21] R.T.Tan, “Visibility in bad weather from a single image”, IEEE Conference on CVPR, 2008.
[22] Z.Tao , S. Changyan1 & W.Xinnian1, “Atmospheric scattering-based multiple images fog removal”, 4th International Congress on Image and Signal Processing, 2011.
[23] A. Buades, Y. Lou, J. M. Morel, & Z. Tang, “Multi image noise estimation and denoising”, (hal.archives-ouvertes.fr),2010.
[24] J. Mao, U. Phommasak, S. Watanabe, H. Shioya, “Detecting Foggy Images and Estimating the Haze Degree Factor”, J Comput Sci Syst Biol, Vol, 7, pp.226-228,2014.
[25] V. Senthamilarasu, A. Baskaran, K. Kutty, “A new approach for removing haze from images,” Processing of the International Conference on Image Processing, Computer Vision and Pattern Recognition (IPCV), 2014.
[26] Peter C, Barnum, Srinivasa Narasimhan, “ Analysis of rain and snow in frequency space,” Int J Comput Vis DOI 10.1007/s11263-008-0200-2
[27] K Garg, S K Nayar, “Detection and removal of rain from videos”, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004

Keywords
Denoising, rain removal, fog removal, image decomposition, Natural image, DWT.