An approach to addressing the stereo correspondence problem is presented using particle swarm optimization algorithm with adaptive hierarchy to obtain a dense disparity map.Firstly

the image features are precisely extracted by using SIFT feature detection

and accurately matched by using SIFT matching algorithm

so the disparity range is rightly and easily calculated from matching features.Secondly

according to restriction of the image size and the disparity range，the coarse to fine adaptive hierarchical image pyramid is built to search fast and reduce wrong matching.Thirdly

a regulation parameter varying with matching window is used to give different power for grayness and smoothness data in optimization function while the matching window is different in dissimilar supporting areas

and improved particle swarm optimization algorithm with variation operation for integer is used to find the fittest solution from a set of potential disparity maps avoiding Genetic algorithm’s blind searching and easy getting in local best solutions.Finally

experimental results on synthetic and real images show that the proposed approach performs dense disparity estimation accurately and quickly.