Stereo matching algorithm based on improved Census transform and dynamic programming

In order to reduce the mismatching rate of binocular stereo matching algorithm in the disparity discontinuity region and under noise disturbance, a stereo matching algorithm based on improved Census transform and dynamic programming is proposed. An improved Census transform with a noise margin is applied to compute the cost based on a cross shape support region. The reliability of single pixel matching cost is enhanced. The guided image filter is used to aggregate the cost volume fast and efficiently. In the disparity selecting step, an improved dynamic programming algorithm is designed to eliminate the scan-line effect and improve the matching speed and accuracy. The final disparity maps are gained after post-processing. The experimental results demonstrate that the proposed algorithm evaluated on the Middlebury benchmark achieves an average error rate of 5.31%, and the accurate disparity can be obtained in both low texture and disparity discontinuity regions with low computing complexity and strong robustness.