Improvement of stereo matching algorithm based on guided filtering and Kernel Regression

Abstract

Stereo matching is one of the most dynamic fields in computer vision. Though its relevant research has already stepped into a mature stage, there are still certain challenges to obtain real-time and high-precision disparity maps from stereo image pairs. This paper presents a novel local stereo matching algorithm with better performance in edge preserving. In the first stage, this paper measures matching cost through combining truncated absolute differences (TAD) of the color and gradient. In the cost aggregation stage, this paper is creatively to combined the weighted guided filtering and adaptive steering kernel regression algorithm, which effectively preserves image edge and depth information. In the final stage, an adaptive steering kernel regression algorithm is employed in interpolation to refine the final disparity map. According to the Middlebury benchmark experiments, the algorithm proposed in this paper could have better performance than other local stereo matching algorithms.