An　analysis　sparse　model　represents　an　image　signal　by　multiplying　it　using　an　analysis　dictionary,　leading　to　a　sparse　outcome.　It　transforms　an　image　(two　dimensional　signal)　into　a　one-dimensional　(1D)　vector.　However,　this　1D　model　ignores　the　two　dimensional　property　and　breaks　the　local　spatial　correlation　inside　images.　In　this　paper,　we　propose　a　two　dimensional　(2D)　analysis　sparse　model.　Our　2D　model　uses　two　analysis　dictionaries　to　efficiently　exploit　the　horizontal　and　vertical　features　simultaneously.　The　corresponding　sparse　coding　and　dictionary　learning　algorithm　are　also　presented　in　this　paper.　The　2D　sparse　model　is　further　evaluated　for　image　denoising.　Experimental　results　demonstrate　our　2D　analysis　sparse　model　outperforms　a　state-of-the-art　1D　analysis　model　in　terms　of　both　denoising　ability　and　memory　usage.