Stereo Matching Algorithm of Multi-Feature Fusion Based on Improved Census Transform

Abstract

This article proposes an improved stereo matching algorithm in order to address the issue that the conventional Census transform is overly dependent on the center pixel of the window, which makes the algorithm susceptible to noise interference and results in low matching accuracy in regions with weak texture and complex texture. In the cost calculation stage, the noise threshold is set utilizing the absolute difference detection approach, and pixels that exceed the threshold are replaced with the mean gray values of the neighboring pixels in the 3 × 3 window. This stage also includes the introduction of the gradient cost, which is coupled with the edge and feature point information to provide the final matching cost. The cross approach is employed to build the adaptive support domain and aggregate the costs during the cost aggregation stage. The disparity is finally calculated using the WTA technique, and a multi-step refinement process is employed to produce the final disparity map. The experiments demonstrate that the proposed algorithm has good anti-noise performance. Compared with other improved algorithms or composite algorithms, the average matching rate of the four standard images on the Middlebury test platform is 5.53%, which is higher than the remaining algorithms, indicating that the matching accuracy is high. The proposed algorithm provides ideas for subsequent improved algorithms.