Optical　flow　estimation　is　one　of　the　key　technologies　in　computer　vision　and　image　processing.　However,　constancy　of　the　grey　value　which　is　used　in　traditional　variational　optical　flow　computation　technology　is　sensitive　to　the　constant　changes　of　illumination　and　non-translational　displacements.　To　solve　this　problem,　the　advanced　data　terms　including　the　gradient　value　constancy　assumptions　and　the　laplacian　constancy　assumptions　are　introduced　in　this　paper.　And　a　flow-based　smoothness　term　is　introduced　to　preserve　the　edges　of　optical　flow.　Additionally,　since　the　model　strictly　refrains　from　a　linearization　of　these　assumptions　and　coarse-to-fine　approaches　are　capable　to　deal　with　large　displacements.　In　the　experiment,　the　efficiency　and　accuracy　of　improved　algorithm　is　verified　with　some　representative　image　sequences.