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Group-Based Sparse Representation for Image Restoration
2014
IEEE Transactions on Image Processing
Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational complexity in dictionary learning. Second, each patch is considered independently in dictionary learning and sparse coding, which ignores the relationship among patches, resulting in inaccurate sparse coding coefficients. In this paper, instead of using patch as the basic unit of sparse representation, we
doi:10.1109/tip.2014.2323127
pmid:24835225
fatcat:eteqjl354rc2baxoipue5vahai