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