High-resolution　(HR)　image　reconstruction　from　single　low-resolution　(LR)　image　is　one　of　the　important　vision　applications.　Despite　numerous　algorithms　have　been　successfully　proposed　in　recent　years,　efficient　and　robust　single-image　super-resolution　(SR)　reconstruction　is　still　challenging　by　several　factors,　such　as　inherent　ambiguous　mapping　between　the　HR-LR　images,　necessary　huge　exemplar　images,　and　computational　load.　In　this　paper,　we　proposed　a　new　learning-based　method　of　single-image　SR.　Inspired　by　simple　mapping　functions　method,　a　mapping　matrix　table　of　HR-LR　feature　patches　is　calculated　in　the　training　phase.　Each　atom　of　dictionary　learned　from　LR　feature　patches　is　corresponding　to　a　mapping　matrix　in　the　mapping　matrix　table.　Combining　this　mapping　table　with　sparse　coding,　high　quality　and　HR　images　are　reconstructed　in　reconstruction　phase.　The　effectiveness　and　efficiency　of　this　method　is　validated　with　experiments　on　the　training　datasets.　Compared　with　state-of-art　methods,　jagged　and　blurred　artifacts　are　depressed　effectively　and　high　reconstruction　quality　is　acquired　with　less　exemplar　images.