Abstract
Sparse representation has shown the effectiveness in solving image restoration and classification problems. To improve the performance of sparse representation, the patch-based and graph-based regularization term with respect to the sparse coding are proposed to solve image restoration and classification problems, respectively. In this paper, the local manifold structure of intensity on patches is exploited by a graph Laplacian operator for performing more precise estimation of sparse coding. Additionally, an improved nonlocal regularization term with the local manifold structure information is proposed to preserve the texture more effectively compared with the traditional nonlocal regularization term. The experimental results on image denoising show the promising performance in terms of both peak signal noise ratio and visual perception.
Published Version
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