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 non-local regularizations, and the down-sampled version of the observed blurry image is used as training samples in the dic-tionary 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.