Adaptive　dictionary　learning　uses　the　low　resolution　image　itself　as　training　samples　to　make　the　similar　patches　have　sparse　representation　over　the　learned　dictionary,　so　that　extra　information　can　be　exploited　from　structural　self-similarity　by　dictionary　learning.　In　this　paper,　we　propose　a　single　image　super　resolution　method　based　on　adaptive　multi-dictionary　learning.　To　exploit　extra　information　from　both　the　low　resolution　image　itself,　and　the　image　database,　the　proposed　method　incorporates　the　idea　of　global　dictionary　learning　that　the　image　database　can　be　used　to　obtain　extra　information　into　the　process　of　adaptive　dictionary　learning.　In　the　proposed　method,　all　patches　in　the　image　pyramid　of　the　low　resolution　image　are　clustered　into　several　groups,　then　each　patch　satisfying　a　certain　condition　in　the　database　is　classified　into　one　of　these　groups　with　the　supervision　of　the　clustering　results,　and　multi-dictionary　learning　is　used　to　learn　corresponding　dictionaries　for　different　groups.　Experimental　results　demonstrate　that　our　method　achieves　better　result　compared　with　ScSR,　SISR,　NLIBP,　CSSS　and　mSSIM　methods.　©,　2015,　Chinese　Institute　of　Electronics.　All　right　reserved.