Image　color　correction　aims　to　eliminate　color　differences　between　images,　especially　for　color　consistency　in　panoramic　or　stereoscopic　images.　Nowadays,　the　global　color　correction　methods　cannot　correct　local　color　differences,　while　the　local　color　correction　methods　usually　lead　to　structural　inconsistency　between　local　regions　and　image　clarity　reduction.　To　address　these　problems,　we　propose　a　matting-based　residual　optimization　for　structurally　consistent　image　color　correction　(MROC).　Inspired　by　the　residual　image,　we　formulate　the　image　color　correction　problem　as　the　optimization　of　a　residual　image　between　the　input　target　image　and　the　resulting　image.　The　residual　image　is　initialized　and　improved　by　the　soft　matting　method　with　a　closed-form　solution.　Besides,　a　data　term　is　introduced　to　identify　those　pixels　with　higher　color　and　structural　consistencies　and　preserve　these　pixels　during　optimization.　The　whole　computational　infrastructure　operates　at　the　pixel　level　to　correct　local　color　differences　while　maintaining　image　clarity.　Experimental　results　demonstrate　that　the　performance　of　the　proposed　MROC　method　is　superior　to　the　state-of-the-art　image　color　correction　methods.　Furthermore,　the　proposed　matting-based　residual　optimization　can　also　be　incorporated　in　a　variety　of　color　correction　methods,　with　enhanced　outcomes　justified　by　a　group　of　image　quality　assessment　metrics.