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.　©　2017　IEEE.