A statistical learning based image denoising approach

Abstract

The image denoising is a very basic but important issue in the field of image procession. Most of the existing methods addressing this issue only show desirable performance when the image complies with their underlying assumptions. Especially, when there is more than one kind of noises, most of the existing methods may fail to dispose the corresponding image. To address this problem, we propose a two-step image denoising method motivated by the statistical learning theory. Under the proposed framework, the type and variance of noise are estimated with support vector machine (SVM) first, and then this information is employed in the proposed denoising algorithm to further improve its denoising performance. Finally, comparative study is constructed to demonstrate the advantages and effectiveness of the proposed method.

Keywords

SVM / image denosing / multiple noises

References

[1] Li J, Ge H. New progress in geometric computing for image and video processing. Frontiers of Computer Science, 2012, 6(6): 769―775

[2] Zhao F, Jiao L, Liu H. Fuzzy c-means clustering with non local spatial information for noisy image segmentation. Frontiers of Computer Science in China, 2011, 5(1): 45―56

[3] Horng S J, Hsu L Y, Li T, Qiao S, Gong X, Chou H H, Khan M K. Using sorted switching median filter to remove high-density impulse noises. Journal of Visual Communication and Image Representation, 2013, 24(7): 956―967

[4] Om H, Biswas M. An improved image denoising method based on wavelet thresholding. Journal of Signal & Information Processing, 2012, 3(1): 17686―8

[5] Chen G, Qian S E. Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(3): 973―980

[6] Kazerouni A, Kamilov U, Bostan E, Unser M. Bayesian denoising: from MAP to MMSE using consistent cycle spinning. IEEE Signal Processing. Letter, 2013, 20(3): 249―252

[7] Kumar V, Kumar A. Simulative analysis for image denoising using wavelet thresholding techniques. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 2013, 2(5): 1873―1878

[8] Gramfort A, Poupon C, Descoteaux M. Denoising and fast diffusion imaging with physically constrained sparse dictionary learning. Medical Image Analysis, 2014, 18(1): 36―49

[9] Ataman E, Aatre V K, Wong K M. Some statistical properties of median filters. IEEE Transactions on Acoustics, Speech and Signal Processing, 1981, 29(5): 1073―1075

[10] Daubechies I, Bates B J. Ten lectures on wavelets. Acoustical Society of America Journal, 1993, 93: 1671

[11] Donoho D L, Johnstone JM. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 1994, 81(3): 425―455

[12] Donoho D L, Johnstone I M, Kerkyacharian G, Picard D. Wavelet shrinkage: asymptopia? Journal of the Royal Statistical Society. Series B (Methodological), 1995: 301―369

[13] Coifman R R, Donoho D L. Translation-invariant de-noising. New York: Springer, 1995

[14] Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing, 2000, 9(9): 1532―1546

[15] Fowler J E. The redundant discrete wavelet transform and additive noise. IEEE Signal Processing Letters, 2005, 12(9): 629―632

[16] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629―639

[17] Catté F, Lions P L, Morel J M, Coll T. Image selective smoothing and edge detection by nonlinear diffusion. SIAM Journal on Numerical Analysis, 1992, 29(1): 182―193

[18] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 1992, 60(1): 259―268

[19] Shao Y, Sun F, Li H, Liu Y. Structural similarity-optimal total variation algorithm for image denoising. In: Proceeding of Foundations and Practical Applications of Cognitive Systems and Information Processing. 2014, 833―843

[20] Kulkarni S, Harman G. An elementary introduction to statistical learning theory. Wiley, 2011

[21] Smola A J, Scholkopf B. A tutorial on support vector regression. Statistics and Computing, 2004, 14(3): 199―222

[22] AbramowitL M, Stegun I A. Handbook of mathematical functions. New York: Dover, 1970

[23] Chang C C, Lin C J. Training v-support vector regression: theory and algorithms. Neural Computation, 2002, 14(8): 1959―1977

[24] Vapnik V. The nature of statistical learning theory. Springer, 2000

[25] Sheikh H R, Sabir M F, Bovik A C. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 2006, 15(11): 3440―3451

[26] Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 2014, 13(4): 600―612

[27] Hore A, Ziou D. Image quality metrics: PSNR vs. SSIM. In: Proceedings of the 20th International Conference on Pattern Recognition (ICPR). 2010, 2366―2369

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg