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http://dx.doi.org/10.6109/jkiice.2017.21.10.1886

Image Denoising Using Nonlocal Similarity and 3D Filtering  

Kim, Seehyun (Department of Information and Communications Engineering, The University of Suwon)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.21, no.10, 2017 , pp. 1886-1891 More about this Journal
Abstract
Denoising which is one of major research topics in the image processing deals with recovering the noisy images. Natural images are well known not only for their local but also nonlocal similarity. Patterns of unique edges and texture which are crucial for understanding the image are repeated over the nonlocal region. In this paper, a nonlocal similarity based denoising algorithm is proposed. First for every blocks of the noisy image, nonlocal similar blocks are gathered to construct a overcomplete data set which are inherently sparse in the transform domain due to the characteristics of the images. Then, the sparse transform coefficients are filtered to suppress the non-sparse additive noise. Finally, the image is recovered by aggregating the overcomplete estimates of each pixel. Performance experiments with several images show that the proposed algorithm outperforms the conventional methods in removing the additive Gaussian noise effectively while preserving the image details.
Keywords
Nonlocal similarity; Denoising; Overcomplete data set; Sparse representation; 3D filtering;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 L. Condat, "A direct algorithm for 1D total variation denoising," IEEE Signal Processing Letters, vol. 20, no. 11, pp. 1054-1057, Nov. 2013.   DOI
2 B.-W. Hong, et. al., "A new model and simple algorithms for Multi-label Mumford-Shah problems," Proc. EEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 1219-1226.
3 A. Buades, et. al, "A non-local algorithm for image denoising," Proc. International Conference on Computer Vision Pattern Recognition, pp. 60-69, 2005.
4 G. Gilboa and S. Osher, "Nonlocal operators with applications to image processing," SIAM Multiscale Modeling & Simulation., vol. 7, no. 3, pp. 1005-1028, 2008.
5 M. Jung, et. al, "Nonlocal Mumford-Shah regularizers for color image restoration," IEEE Trans., Image Processing, vol. 20, no. 6, pp. 1583-1598, Jun. 2011.   DOI
6 S. Shan, et. al., "BM3D denoising based on minimum GCV score," Proc. International Conference on Computers, Communications, and Systems, pp. 154-158, 2015.
7 M. Nilchian, et. al, "Optimized Kaiser-Bessel window functions for CT," IEEE Trans., Image Processing, vol. 24, no. 11, pp. 3826-3833, Dec. 2015.   DOI
8 J. Zhang, et. al., "Image restoration using joint statistical modeling in a space-transform domain," IEEE Trnas., Circuits and Systems for Video Technology, vol. 24, no. 6, pp. 915-928, June 2014.   DOI
9 J. A. Guerrero-Colon and J. Portilla, "Two-level adaptive denoising using Gaussian scale mixtures in overcomplete oriented pyramids," Proc. IEEE International Conference on Image Processing, pp. 105-108, Sep. 2005.
10 R. Gonzalez and R. Woods, Digital Image Processing, 3rd Ed., Pearson, New Jersey 2010.
11 S. Kim, "A compressive sensing based imaging algorithm using incoherent measurements and DCT," Journal of the Korea Institute of Information and Communication Engineering, vol. 20, no. 10, pp. 1961-1966, Oct. 2016.   DOI