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http://dx.doi.org/10.7471/ikeee.2014.18.4.523

Fast Blind Image Denoising Algorithm Based on Estimating Noise Parameters  

Nguyen, Tuan-Anh (School of Electronic Engineering, Soongsil University)
Kim, Beomsu (School of Electronic Engineering, Soongsil University)
Hong, Min-Cheol (School of Electronic Engineering, Soongsil University)
Publication Information
Journal of IKEEE / v.18, no.4, 2014 , pp. 523-531 More about this Journal
Abstract
In this paper, a fast single image blind denoising algorithm is presented, where noise parameters are estimated by local statistics of an observed degraded image without a prior information about the additive noise. The estimated noise parameters are used to define the constraints on the noise detection which is coupled with the 1st-order Markov Random Field. In addition, an adaptive modified weighted Gaussian filter is introduced, where variable window sizes and weighting coefficients defined by the constraints are used to control the degree of the smoothness of the reconstructed image. The experimental results demonstrate the capability of the proposed algorithm. Please put the abstract of paper here.
Keywords
denoising; noise parameters; constraints; modified Gaussian filter; smoothness;
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1 A. Buades, B. Coll, and J. M. Morel, "Image denosing methods. A new nonlocal principle," SIAM Review, vol. 52, no. 1, pp. 113-147, 2010.   DOI
2 K. Dabov, A. Foi, V. Katkovnik, and K. O. Egiazarian, "Image denoising by sparse 3D transform domain collaborative filtering," IEEE Trans. Image Processing, vol. 16, no. 8, pp. 2080-2095, 2007.   DOI   ScienceOn
3 X. Zhang, X. Feng, and W. Wang, "Two-direction nonlocal model for image denoising," IEEE Trans. Image Processing, vol. 22, no. 1, pp. 408-412, 2013.   DOI
4 G. L. Anderson and A. K. Netravali, "Image restoration based on a subjective criterion," IEEE Trans. Sys., Man and Cybern., vol. 6, no. 12, pp. 845-853, 1976.   DOI   ScienceOn
5 S. I. Olsen, "Noise variance estimation in images," Computer Vision, Graphics and Image Processing, vol. 55, no. 4, pp. 319-323, 1993.
6 D. H. Shin, R. H. Park, and S. J. Yang, "Block-based noise estimation using adaptive Gaussian filtering," IEEE Trans. Consumer Electronics, vol 51, no. 1, pp. 218-226, 2005.   DOI   ScienceOn
7 Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simmoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Trans. Image Processing, vol. 13, no. 4, pp. 600-612, 2004.   DOI   ScienceOn
8 E. Farzana, M. Tanzid, K. Modhin, and M. I. H. Bhuiyan, "Adaptive bilateral filter for image denoising," SPIE Int. Conf. on Graphic and Image Processing, vol. 8285, doi:10.1117/12.913419, 2011.   DOI
9 Z. Wang and D. Zang, "Progressive switching median filter for removal of impulse noise from highly corrupted images," IEEE Trans. Circuits and Systems II, vol. 46, no. 1, pp. 78-80, 1999.   DOI   ScienceOn
10 B. Zhang and J. P. Allebach, "Adaptive bilateral filter for sharpness enhancement and noise removal," IEEE Trans. Image Processing, vol. 17, no. 5, pp. 664-678, 2008.   DOI   ScienceOn
11 J. H. Lee, Y. H. Kim, and J. H. Nam, "Adaptive noise reduction algorithm based on statistical hypotheses tests," IEEE Trans. Consumer Electronics, vol. 54, no. 3, pp. 1406-1414, 2008.   DOI   ScienceOn
12 V. R. Vijaykumar, P. T. Vanathi, and P. Kanagasabapathy, "Fast and efficient algorithm to remove Gaussian noise in digital images," IAENG Int' J. of Computer Science, vol. 37, no. 1, pp. 300-302, 2010.
13 H. Li, P. Fan, and M. Khan, "Context-adaptive anisotropic diffusion for image denoising," IET Electronic Letters, vol. 48, no. 14, pp. 827-829 2012.   DOI
14 P. Milanfar, "A tour of modern image filtering," IEEE Signal Processing Magazine, vol, 30, no. 1, pp. 106-128, 2013.   DOI
15 A. K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989