Stereo matching using pixel classification and dual-weighted guided filter

Finding the subjectively correct binocular disparity of a stereopair has long been a challenging issue for researchers. A coarse-to-fine algorithm is presented in this article for seeking to a more reasonable solution to this issue. We focus on the disparity computation other than disparity refinement and post-processing procedures. Dual-weighted guided filter or a simplified guided image filter with dual-weights is proposed to boost computation efficiency and reduce matching ambiguity. Perceived as a classification step, a normalized cross-correlation based method aims for searching for reliable matching candidates. Three channels in RGB colour space are biologically weighted for subjectively rational correspondence. As comparative experimental results show, the proposed method performs excellently without post-processing. All the algorithms are tested on Middlebury stereopsis benchmark.