In stereo matching, energy-minimization optimizers such as dynamic programming (DP) could achieve efficient disparity estimation. However, DP-based optimizers still suffer from relatively high energy due to too many artificial penalty parameters and a complex energy function, inevitably leading to matching failure in depth discontinuities, occlusions, and low-textured regions. To reach dense high-accuracy disparity estimation, we propose an effective stereo-matching method using texture discrimination-enforced matching cost computation and smoothness-weighted cost regularization. First, pixel adjustment is introduced to the gradient difference calculation so that initial matching accuracy, especially in low-textured regions, can be improved. For determining the degree of pixel adjustment, a gray-gradient co-occurrence matrix and the fuzzy c-means method are adopted to differentiate between two varieties of reference images based on textural property. Second, the support weight in local methods and the smoothness constraints in global optimizations are leveraged. Color information and the confidence map of the input image are combined to form the support weight. By ameliorating the smoothness constraint function during weighted horizontal DP passes, the adverse effects of excessive penalty can be reduced. Third, a postprocessing method is proposed for refining results in both occluded and low-textured regions. The proposed method considers test cases from the Middlebury v.2 and v.3 datasets and can outperform the state-of-the-art stereo-matching algorithms.