Disparity Optimization Algorithm for Stereo Matching Using Improved Guided Filter

Abstract

A disparity optimization algorithm based on an improved guided filter is proposed to smooth the disparity image. A well-known problem to local stereo matching is the low matching accuracy and staircase effect in regions with weak texture and slope. Our disparity optimization method solves this problem and achieve a smooth disparity. First, the initial disparity image is obtained by a local stereo matching algorithm using segment tree. Then, the guided filter is improved by using gradient domain information. Lastly, the improved guided filter is adopted as the disparity optimization method to smooth the disparity image. Experiments conducted on the Middlebury data sets demonstrate that by using the proposed algorithm in this paper, the smoothness of the disparity map in slope regions is improved, and a higher precision of dense disparity is obtained.