传统的立体匹配算法大都基于两幅图像像素点或者局部块的对应性，在单一尺度下求取视差图，但这不能很好地建模低纹理及重复纹理区域的对应关系，致使获得的视差图精度有限。为了改善上述问题，考虑到人眼视觉系统在不同尺度上处理所接收到的视觉信号，提出了跨尺度的重启动与随机游走算法。首先计算场景图像的匹配代价，其次利用超像素分割进行快速初始聚合，然后使用重启动与随机游走算法对其进行全局上的优化，最后采用跨尺度模型实现匹配代价的有效融合更新，继而获取场景图像的视差图。在Middlebury 数据集上的实验仿真结果表明，相较于传统的跨尺度立体匹配算法，该算法能够有效地将场景图像在所有区域及非遮挡区域的加权平均误匹配率分别降低 1 个百分点和 3 个百分点，获得高精度的视差图。

Traditional stereo matching algorithms are mostly based on the correspondence between two image pixels or partial blocks，finding the disparity map at a single scale． But these algorithms can not model the correspon-dence between low-texture and repeated texture regions，resulting in a limited accuracy of the obtained disparity map． Considering the human visual system processes the received visual signals on different scales，a cross-scale restart and random walk algorithm was proposed to improve the above problems． Firstly，the matching cost of the scene images was calculated． Then using super pixel segmentation for rapid initial aggregation，and using the re-start and random walk algorithm to optimize it globally． Finally，an effective fusion update of the matching cost was realized by adopting the cross-scale model，and then a disparity map of the scene image was obtained． The ex-perimental results on the Middlebury dataset show that，compared with the traditional cross-scale stereo matching algorithm，the proposed algorithm can effectively reduce the average mismatch rate of the scene image in all regions and non-occluse regions by 1 percentage and 3 percentage points respectively，and obtain high-precision disparity
map．