The　spatial　resolution　of　the　image　is　limited　by　many　factors　including　the　environment　of　the　imaging,　the　manufacturing　technology　of　the　hardware　and　the　cost.　A　codebook　mapping　based　single　image　super　resolution　(SR)　method　via　dictionary　learning　and　structural　self-similarity　is　proposed　to　promote　the　spatial　resolution　of　the　image.　Firstly,　the　images　in　the　training　set　are　used　to　construct　two　codebooks　corresponding　to　the　low　and　the　high　frequency　components　of　image　patches,　and　the　two　codebooks　are　taken　as　the　training　samples　to　learn　two　dictionaries　respectively.　Then,　the　similar　image　patches　of　each　input　image　patch　in　the　initial　reconstructed　image　are　used　to　construct　the　nonlocal　constraint.　Finally,　the　sparse　representation　coefficient　of　the　input　image　patch　is　obtained　by　solving　an　l0　norm　minimization　problem　with　the　nonlocal　constraint,　and　the　high　resolution　image　patch　is　then　reconstructed　by　using　the　low　and　the　high　dictionaries.　The　input　image　patch　is　represented　by　the　dictionaries,　rather　than　the　codebooks,　therefore　the　computational　efficiency　is　increasing.　Similar　image　patches　are　used　to　construct　the　regularization　constraint,　consequently　the　quality　of　the　reconstructed　image　is　improved.　Experimental　results　demonstrate　that　the　proposed　method　achieves　better　reconstructed　results　compared　with　LLE,　ScSR　and　NARM　methods.　©,　2015,　Institute　of　Computing　Technology.　All　right　reserved.