Image　inpainting　is　a　technique　that　aims　to　recovering　missing　or　damaged　regions　of　an　image　without　any　manual　intervene.　Conventional　exemplar-based　approaches　suffer　two　major　defects.　One　defect　is　that　the　inpainting　quality　heavily　depends　on　the　extent　of　self-similarity.　If　no　similar　samples　exist　in　the　already　known　region　of　the　image,　the　missing　region　may　be　filled　by　irrelevant　ones,　which　lead　to　an　unexpected　result.　The　other　is　that　the　process　of　finding　the　best-match　sample　is　computationally　intensive.　In　this　paper,　we　introduce　an　exemplar-based　image　inpainting　algorithm　by　exploiting　structured　sparse　representation　techniques　to　overcome　the　limitations　mentioned　above.　The　filling-in　patch　is　estimated　based　on　a　composite　dictionary　instead　of　an　exhausted　search.　The　experimental　results　show　that　the　proposed　method　achieves　a　good　visual　quality.　©　2013　CSREA　Press.　All　rights　reserved.