Stereoscopic　3D　(S3D)　image　color　correction　is　a　major　issue　in　the　field　of　image　processing.　However,　existing　color　correction　algorithms　have　limitations.　Global　color　correction　algorithms　cannot　handle　local　color　discrepancies,　and　local　color　correction　algorithms　are　sensitive　to　matching　quality　between　reference　and　target　images.　In　this　study,　we　propose　an　S3D　image　color　correction　algorithm　that　combines　global　and　local　color　information　to　correct　color　discrepancies　between　S3D　images.　Sparse　feature　matching　usually　generates　only　a　few　matching　features,　producing　error　correction　results　in　some　local　regions.　Our　algorithm　uses　dense　stereo　matching　and　global　color　correction　algorithms　to　initialize　color　values,　and　improves　the　local　color　smoothness　and　global　color　consistency　of　the　resulting　image,　while　maintaining　the　initial　color　in　that　image　as　much　as　possible.　Experimental　results　show　that　our　algorithm　performs　better　than　do　five　state-of-the-art　color　correction　algorithms.　©　2017　IEEE.