Local stereo matching algorithm with efficient matching cost and adaptive guided image filter

To make a matching algorithm to satisfy the requirements of high precision and anti-interference, a novel stereo-matching algorithm with efficient matching cost and adaptive guided image filter is proposed. Firstly, we adopt a modified Census transform with a local texture metric to compute the initial cost. It can make full use of the cross-correlation information between pixels. Meanwhile, we incorporate the Census, color and gradient costs as a mixed matching cost algorithm. Then, we aggregate the costs with guided image filter based on adaptive rectangular support window instead of the traditional fixed support window. The variable kernel window is constructed by the local color similarity and spatial distance. In this way, less occluded points will be included in the support region. On this basis, we adopt integral image to further speed up the computation of this step. Finally, the initial disparity of each pixel is selected using winner takes all optimization and the final disparity maps are gained after post-processing. The experimental results demonstrate that the proposed algorithm not only achieves an average error rate of 5.22 % on the Middlebury stereo benchmark data set, but can also overcome the influence of illumination distortion in the matching effectively.