Alternating group sparsity for image restoration
نویسندگان
چکیده
Recently, collaborative image filtering based on groupbased sparse representation has gained a popularity in image restoration. BM3D frame [1], one of the first example of such a representation, utilizes both local sparsity of small size image patches and group-sparsity of collections of selfsimilar image patches. As a sparsifying transforms in the spatial and similarity domains, fixed transforms (e.g., DCT or wavelets) or data-adaptive transforms (obtained by SVD or PCA) [4] can be used. Modern image restoration methods utilize l0 and l1 minimization frameworks applying convex optimization algorithms, e.g. iterative shinkage-thresholding, split Bregman (SB), etc. In [5], group sparse representation (essentially, BM3D frame with SVD as a sparsifying transform [4]) is used in the framework of SB, whereas in [1] BM3D frames with fixed sparsifying transform have been used.
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