Blind　image　deconvolution,　i.e.,　estimating　both　the　latent　image　and　the　blur　kernel　from　the　only　observed　blurry　image,　is　a　severely　ill-posed　inverse　problem.　In　this　paper,　we　propose　a　blur　kernel　estimation　method　for　blind　motion　deblurring　using　sparse　representation　and　cross-scale　self-similarity　of　image　patches　as　priors　to　recover　the　latent　sharp　image　from　a　single　blurry　image.　Sparse　representation　indicates　that　image　patches　can　always　be　represented　well　as　a　sparse　linear　combination　of　atoms　in　an　appropriate　dictionary.　Cross-scale　self-similarity　results　in　that　any　image　patch　can　in　some　way　be　well　approximated　by　a　number　of　other　similar　patches　across　different　image　scales.　Our　method　is　based　on　the　observations　that　almost　any　image　patch　in　a　natural　image　has　multiple　similar　patches　in　down-sampled　versions　of　the　image,　and　down-sampling　produces　image　patches　that　are　sharper　than　those　in　the　blurry　image　itself.　In　our　method,　the　dictionary　for　sparse　representation　is　trained　adaptively　from　sharper　patches　sampled　from　the　down-sampled　latent　image　estimate　to　make　the　similar　patches　of　the　latent　sharp　image　well　represented　sparsely,　and　meanwhile,　all　patches　from　the　latent　image　estimate　are　optimized　to　be　as　close　to　the　sharper　similar　patches　searched　from　the　down-sampled　version　to　enforce　the　sharp　recovery　of　the　latent　image　by　constructing　a　non-local　regularization.　Experimental　results　on　both　simulated　and　real　blurry　images　demonstrate　that　our　method　outperforms　state-of-the-art　blind　deblurring　methods.