Vast computation is a great disadvantage of the existing graph cuts based vision algorithms. Lack of adaptability is another issue. An improved global optimal algorithm for dense disparity mapping using graph cuts is presented in this paper. First, adapted occlusion penalty and smoothness penalty are defined based on the intrinsic relation between the disparity changes and the discontinuities in an image. The graph cuts based algorithm is employed to get an optimial dense disparity mapping with occlusions. Secondly, according to the complexity analysis of graph cut algorithms, an operation named restricted α-expansion operation is defined to control the vertexes generation during graph constructing based on the result of normalized correlation algorithm. It is a great help to reduce the vertexes and edges in the constructed graph, thus the computing is speeded up. The experimental results show performance of the proposed algorithm is improved and it will take a shorter time to compute an accuracy dense disparity mapping.