Abstract：Aiming at insufficient sampling of image patches in the process of over-complete dictionary training of sparse representation model, an algorithm of image patch transform training and non-convex regularization for image denoising and deblurring is proposed. The image patch search strategy with inter-group variance constraint is adopted, and the selected dictionary set is transposed and learned according to the adaptive soft threshold. The lp(0<p<1) norm is adopted in the reconstruction process to ensure strong sparsity of the results. Split Bregman method is employed to solve the proposed non-convex model. Experimental results show that the proposed algorithm produces better visual effect and Denoising and Deblurring effect.
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2019, Vol. 32 
