Image　super-resolution　based　on　sparse　model　with　patch　clustering　and　nonlocal　similarity　provides　promising　performance.　However,　the　traditional　one　dimensional　(1D)　sparse　model　enforces　a　1D　dictionary　for　every　cluster　of　patches　to　capture　complex　structures　and　different　features　in　images.　The　total　dictionary　will　take　expensive　memory,　which　can　be　alleviated　at　cost　of　representation　power.　Recently,　two　dimensional　(2D)　sparse　model　has　been　proved　to　efficiently　represent　images　and　save　memory　usage.　In　this　paper,　we　propose　to　integrate　2D　sparse　model　with　patch　clustering　and　nonlocal　similarity　into　a　variational　framework　as　2D　nonlocal　sparse　representation　(2DNSR)　for　image　SR　to　save　memory　cost　and　ensure　SR　performances.　We　also　present　a　2DNSR　algorithm　for　image　SR　where　each　group　of　similar　patches　decompose　on　the　respective　2D　dictionaries.　Experimental　results　on　image　SR　demonstrate　our　proposed　2D　nonlocal　representation　outperforms　2D　sparse　model　and　achieves　competitive　performance　to　state-of-the-art　1D　nonlocal　sparse　models　whereas　with　much　less　memory　costs.