27 October 2013 Image denoising via group Sparse representation over learned dictionary
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Proceedings Volume 8919, MIPPR 2013: Pattern Recognition and Computer Vision; 891916 (2013) https://doi.org/10.1117/12.2032051
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
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, Pan Cheng, Chengzhi Deng, Chengzhi Deng, Shengqian Wang, Shengqian Wang, Chunfeng Zhang, 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); doi: 10.1117/12.2032051; https://doi.org/10.1117/12.2032051
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