As　a　powerful　statistical　signal　modeling　technique,　sparse　representation　has　been　widely　used　in　various　image　restoration　(IR)　applications.　The　sparsity-based　methods　have　achieved　leading　performance　in　the　past　few　decades.　However,　in　recent　years　it　has　been　surpassed　by　other　methods,　especially　the　recent　deep　learning　based　methods.　In　this　paper,　we　address　the　question　that　whether　sparse　representation　can　be　competitive　again.　The　way　we　answer　this　question　is　to　redesign　it　with　a　deep　architecture.　To　be　specific,　we　propose　an　end-to-end　deep　architecture　that　follows　the　process　of　the　sparse　representation　based　IR.　In　particular,　we　learn　a　sparse　convolutional　dictionary　to　replace　the　traditional　dictionary,　and　a　convolutional　neural　network　(CNN)　denoising　prior　to　replace　the　image　prior.　Through　end-to-end　training,　the　parameters　in　convolutional　dictionary　and　CNN　denoiser　can　be　jointly　optimized.　Experimental　results　on　several　representative　IR　tasks,　including　image　denoising,　deblurring　and　super-resolution,　demonstrate　that　the　proposed　deep　network　can　achieve　superior　performance　against　state-of-the-art　model-based　and　learning-based　methods.