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Stereo matching is a challenging issue in computer vision field. To address the poor accuracy behavior of local algorithms, we propose an improved stereo matching algorithm based on guided image filter. Firstly, we put forward a combined matching cost by incorporating the absolute difference and improved color census transform (ICCT). Secondly, we use the guided image filter to filter the cost volume, which can aggregate the costs fast and efficiently. Then, in the disparity computing step, we design a modified dynamic programming algorithm, which can weaken the scanning line effect. At last, the final disparity maps are gained after post-processing. The experimental results are evaluated on the Middlebury stereo dataset, showing that our approach can achieve good results both in low texture and depth discontinuity areas with an average error rate of 5.14%. Keywords-stereo matching; census transform; guided image filter; dynamic programming

An Improved Stereo Matching Algorithm Based on Guided Image Filter