Research on semiglobal stereo matching algorithm based on multicost

Semi-global stereo matching (SGM) algorithm is widely adopted in stereo matching due to its optimal trade-off between accuracy and efficiency. However, SGM exhibits limitations in accurately matching weak texture regions and entails high computational complexity. The present paper proposes a novel enhanced SGM algorithm by integrating the CT cost and BT cost, aiming to address this issue. Initially, the anti-interference capability of Census transform is improved by setting a standard deviation threshold. The fused weights of the Census cost and window-based BT cost compensate for insufficient image information. Subsequently, an 8-channel dynamic programming algorithm is utilized to aggregate costs followed by a winner-take-all approach to compute disparity values. Furthermore, a weighted least squares filter optimizes the disparity map. Finally, the proposed algorithm's anti-occlusion performance and matching accuracy are evaluated using the Middlebury dataset. Experimental results demonstrate that our proposed cost calculation method outperforms CT cost and BT cost in terms of anti-interference performance significantly when compared with both SGM algorithm and AD-Census algorithm.