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27 October 2013Image denoising via group Sparse representation over learned dictionary
Images are one of vital ways to get information for us. However, in the practical application, images
are often subject to a variety of noise, so that solving the problem of image denoising becomes
particularly important. The K-SVD algorithm can improve the denoising effect by sparse coding atoms
instead of the traditional method of sparse coding dictionary. In order to further improve the effect of
denoising, we propose to extended the K-SVD algorithm via group sparse representation. The key point
of this method is dividing the sparse coefficients into groups, so that adjusts the correlation among the
elements by controlling the size of the groups. This new approach can improve the local constraints
between adjacent atoms, thereby it is very important to increase the correlation between the atoms. The
experimental results show that our method has a better effect on image recovery, which is efficient to
prevent the block effect and can get smoother images.
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Pan Cheng, Chengzhi Deng, Shengqian Wang, Chunfeng Zhang, "Image denoising via group Sparse representation over learned dictionary," Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891916 (27 October 2013); https://doi.org/10.1117/12.2032051