立体匹配技术是计算机视觉领域的研究热点，由于问题本身的病态性，一直没有得到很好地解决。针对现有局部立体匹配算法精度不高以及易受光照失真影响的问题，提出了一种基于改进梯度匹配代价和自适应窗口的匹配算法。在传统梯度向量仅包含幅度信息的基础上，引入相位信息，并对原始匹配代价进行变换，进一步消除异常值；利用图像结构和色彩信息构建自适应窗口进行代价聚合；提出了一种局部视差直方图的视差精化方法，获得了高精度的视差图。实验结果表明，所提算法在Middlebury 测试平台上平均误匹配误差为6.1%，且对光照失真条件具有较高的稳健性。

Stereo matching is one of the most active research areas in computer vision. As it is an ill problem, a perfect solution does not exist until now. Aiming at solving the problem of low accuracy and sensitivity to radiometric distortion caused by existing local matching algorithm, a new stereo matching algorithm using improved gradient cost and adaptive window is presented here. Besides of the magnitude information in traditional gradient cost, the phase information is introduced and the taw matching cost is transformed to eliminate outliers. An adaptive window for every pixel is constructed by utilizing the image structure and color intensity. An effective disparity refinement method based on local disparity histogram is employed, which gains disparity maps with high accuracy. The experimental results show that the proposed method has an average error of 6.1% in the Middlebury testing benchmark, while keeping strong robustness to radiometric distortion.