为提高双目立体匹配算法在弱纹理区域的匹配精度和多尺度空间的匹配一致性，提出基于窗口内像素均值信息判断和自适应权重的改进 Census 变换算法进行代价计 算，提高像素在视差不连续区域的匹配精度．代价聚合阶段引入高斯金字塔结构，将引导图滤波算法融合到多尺度模型中，并添加正则化约束来提高对弱纹理区域的匹配一致性; 视差选择阶段中，采用一系列优化方法如误匹配点检测、区域投票策略和亚像素增强等来 提高匹配的正确率．实验结果表明，该算法在Middlebury 测试集上的平均误码率为 5．91%，在弱纹理区域和视差不连续区域能得到较好的视差图，且具有较好的鲁棒性．

In order to improve the matching accuracy of the stereo matching algorithm in a low-texture region and its cost volume consistency across multiple scales,an improved Census transform algorithm is proposed，the judge and self-adaptation of weights are conducted based on the mean information of the pixels in a window,and the matching accuracy of the pixels in a discontinuity region is improved by performing a cost calculation.Further- more,the Gaussian Pyramid is introduced in cost aggregation,the Guided image filter algorithm is integrated with the multiple-scale model，and the regularization constraint is also added to strengthen the cost volume consistency with the low-texture region.In the disparity selecting step,a series of optimization methods,such as the outlier detection，the region voting and the sub-pixel enhancement,are used to improve the correct rate of the matching.Experimental results show that the proposed algorithm achieves an average error rate of 5. 91% in the Middlebury benchmark，with a better disparity map in the low-texture and discontinuity regions,and that it is of a strong ro- bustness.