Blind image deblurring based on sparse representation and structural self-similarity

In this paper, we propose a blind motion deblurring method based on sparse representation and structural self-similarity from a single image. The priors for sparse representation and structural self-similarity are explicitly added into the recovery of the latent image by means of sparse and multi-scale nonlocal regularizations, and the down-sampled version of the observed blurry image is used as training samples in the dictionary learning for sparse representation so that the sparsity of the latent image over this dictionary can be guaranteed, which implicitly makes use of multi-scale similar structures. Experimental results on both simulated and real blurry images demonstrate that our method outperforms existing state-of-the-art blind deblurring methods.