A stereo-matching algorithm using weighted guided image filtering based on the Laplacian of Gaussian (LoG) operator is proposed. The algorithm calculates matching cost by fusing weighted absolute difference and gradient. Then, cost aggregation is implemented using an improved guided image filtering based on the LoG operator to ensure that the penalty parameter is self-adaptive. The disparity computation is implemented using the winner-take-all (WTA) strategy, and the final disparity map is obtained using two different interpolation methods. The experimental results show that the average mismatch rate of the proposed algorithm on the Middlebury benchmark standard dataset is 4.32%. The proposed algorithm can process both high and low texture regions effectively; thus, the mismatch rate of the disparity map is reduced.