Abstract

This paper investigates image denoising algorithm based on sparse representation with over-complete dictionary. Here K-SVD(Singular Value Decomposition) algorithm is used to train the over-complete dictionary, followed by OMP(Orthogonal Matching Pursuit) algorithm for image sparse decomposition. While this strategy achieves good performance on image denoising, it has high computational complexity. In order to speed up computation, Batch-OMP instead of OMP algorithm is adopted to improve the denosing algorithm, which significantly shortens the running time. Finally, analysis is made on the configuration of parameters versus the performance in the denoising algorithm; we use particle swarm optimization algorithm to learn the best parameters.

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