Two　dimensional　(2D)　sparse　representation　provides　promising　performance　in　image　denoising　by　cooperatively　exploiting　horizontal　and　vertical　features　inherent　in　images　by　two　dictionaries.　In　this　paper,　we　first　propose　integrating　the　2D　sparse　model　with　clustering　and　nonlocal　regularization　into　a　unified　variational　framework,　defined　as　2D　nonlocal　sparse　representation　(2DNSR),　for　optimization.　Within　this　framework,　we　then　present　a　dictionary　learning　method　for　image　denoising　which　jointly　decomposes　groups　of　similar　noisy　patches　on　subsets　of　2D　dictionaries.　We　finally　present　a　2DNSR-based　algorithm　for　image　denoising.　Experimental　results　on　image　denoising　show　our　proposed　2D　nonlocal　sparse　representation　outperforms　the　2D　sparse　model　and　achieves　competitive　performance　to　state-of-the-art　nonlocal　sparse　models　whereas　with　much　less　memory　costs.