Abstract
An image deconvolution approach based on regularized sparse representation is proposed. Given an observed blurred image, traditional deconvolution approach based on sparse representation constructs the -norm regularization directly by using the direct observed model and the sparsity of image decomposed on a redundant dictionary. However, the -norm regularization has large coherence because of the compactness of the blurring operator. Therefore, we multiply the blurred image with a regularizing operator and construct the -norm regularization by using the converted data. The approach, referred as regularized sparse representation, possesses the following merits: (i) it decreases the coherence of the -norm regularization; (ii) algorithm solving the regularization is fast converged due to the preconditioning strategy. Experimental results demonstrate that comparing with existed state-of-the-art approaches, the proposed approach can obtain good results in terms of speed and deconvolution performance.
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