Sparse　representation　has　been　proved　to　be　very　efficient　in　machine　learning　and　image　processing.　Traditional　image　sparse　representation　formulates　an　image　into　a　one　dimensional　(1D)　vector　which　is　then　represented　by　a　sparse　linear　combination　of　the　basis　atoms　from　a　dictionary.　This　1D　representation　ignores　the　local　spatial　correlation　inside　one　image.　In　this　paper,　we　propose　a　two　dimensional　(2D)　sparse　model　to　much　efficiently　exploit　the　horizontal　and　vertical　features　which　are　represented　by　two　dictionaries　simultaneously.　The　corresponding　sparse　coding　and　dictionary　learning　algorithm　are　also　presented　in　this　paper.　The　2D　synthesis　model　is　further　evaluated　in　image　denoising.　Experimental　results　demonstrate　our　2D　synthesis　sparse　model　outperforms　the　state-of-the-art　1D　model　in　terms　of　both　objective　and　subjective　qualities.