Blind　image　deconvolution　aims　to　recover　the　latent　sharp　image　from　a　blurry　image　when　the　blur　kernel　is　unknown.　Since　blind　deconvolution　is　an　underdetermined　problem,　existing　methods　take　advantage　of　various　prior　knowledge　directly　or　indirectly.　In　this　article,　we　propose　a　single　image　blind　deconvolution　method　based　on　sparse　representation　and　structural　self-similarity.　In　our　method,　we　add　the　image　sparsity　prior　and　structural　self-similarity　prior　to　the　blind　deconvolution　objective　function　as　regularization　constraints,　and　we　utilize　the　structural　self-similarity　between　different　image　scales　by　taking　the　down-sampled　version　of　observed　blurry　image　as　the　sparse　representation　dictionary　training　set　so　that　the　sparsity　of　the　latent　sharp　image　under　this　dictionary　can　be　ensured.　Finally,　we　estimate　the　blur　kernel　and　sharp　image　alternately.　Experimental　results　on　both　simulated　and　real　blurry　images　demonstrate　that　the　blur　kernels　estimated　by　our　method　are　accurate　and　robust,　and　that　the　restored　images　have　high　visual　quality　with　sharp　edges.　Copyright　©　2017　Acta　Automatica　Sinica.　All　rights　reserved.