An Image Restoration Method with Independently Local Dictionary Learning

Background: Recently, sparse representation has been significantly used in various image inverse problems, such as image deblurring, super resolution and compressive sensing, and has shown promising results. The key issue of sparse representation is how to find a reasonable dictionary, by which the image can present more sparsity, as described in various patents. Method: In this paper, we address the image restoration and propose a novel cost function. Considering the significant difference of underlying structure within different patches, we independently train the dictionary using a set of self-similarity patches to present each patch more sparsely. Result: To solve the proposed cost function, an approach based on alternating optimization is presented to obtain the approximate solution. Conclusion: Experimental results demonstrate that the proposed method is superior to many existing excellent algorithms.