Adaptive Stereo Matching Based on Local Information Entropy and Improved AD-Census Transform

To enhance the accuracy of traditional local stereo matching algorithms in weak-texture regions and address the limitations of the AD-Census transform in adapting to local region features during cost fusion, an adaptive stereo matching algorithm based on local information entropy and an improved AD-Census transform is proposed. In the cost calculation stage, the local information entropy of the input image is first computed. Then, based on the entropy of the pixel neighborhood, an adaptive window size is selected to refine the Census transform. Next, an adaptive fusion weight is determined from the local information entropy to combine the improved Census and AD costs. In the cost aggregation stage, a unidirectional dynamic programming aggregation algorithm is introduced. After disparity computation and optimization, the final disparity map is produced. The algorithm is evaluated on the Middlebury platform using standard test images. Experimental results indicate that the proposed algorithm achieves an average mismatch rate of 5. 94 % in non-occluded areas and 8. 37 % across all areas, outperforming many existing algorithms in terms of matching quality and robustness to noise.