To address the difficulty of the conventional binocular-stereo-matching algorithm in effectively solving the ambiguity of weak-texture-region matching, a matching cost aggregation-diffusion method based on region division is proposed. First, the texture features of the left and right images, which are classified into rich- and weak-texture regions, are analyzed. Subsequently, a boundary-preserving filtering algorithm is used to aggregate the matching costs. Finally, the matching costs of pixels in the rich-texture regions are diffused to the weak-texture regions based on the local-texture similarity to improve the accuracy of pixel stereo matching in the weak-texture regions. Test results based on datasets from the Middlebury website show that, after introducing the proposed method, the false match rate of non-occluded regions in the test image of the 2001 and 2003 datasets reduced by 1.01 percentage points on average, whereas the false match rate of the entire image reduced by 1.10 percentage points on average. In the test images of the 2005 and 2006 datasets, the false match rate of the non-occluded area reduced by 1.87 percentage points on average, whereas the false match rate of the entire image reduced by 1.87 percentage points on average. In the test images of the 2014 dataset, the false match rate of the non-occluded area reduced by 1.30 percentage points on average, whereas the false match rate of the entire image reduced by 1.31 percentage points on average. These results show that the proposed method can effectively improve the accuracy of stereo matching.