Matching cost function analysis and disparity optimization for low-quality binocular images

State-of-the-art dense stereo matching algorithms have achieved excellent performance, demonstrating a capability to attain precise matching in most areas. However, current such methods rarely achieve this when images are captured under poor conditions. To improve the accuracy of the algorithm in such cases, this paper introduces a post-optimization algorithm to rectify matching errors and enhance outcomes. The main research areas of this paper include three aspects. (1) Disparities are classified into reliable and unreliable results based on the analysis of geometric matching relationships, local features in the images, and components within the matching cost function; (2) Subsequent analysis of horizontal image features identifies local characteristic indices calculated through integration along the horizontal axis, which establish specific matching criteria, forming the foundation for a cost volume that encompasses these distinct matches; (3) A redefined matching cost function is applied to previously classified unreliable results to rectify matching errors. This energy function is based on the cost volume above. Experimental results validate the efficacy of the proposed post-optimization algorithm, reducing the average matching errors from 8.66% to 5.85%.