Adaptive window stereo matching algorithm for weak texture region

Stereo matching is one of the key techniques in stereo vision. Due to the existing adaptive window stereo matching algorithm is insufficient to extract features in low-texture regions, a weighted dynamic adaptive window stereo matching algorithm based on pixel gradient value is proposed. Firstly, the Sobel operator is used to calculate the gradient value of each pixel in the image and the phase information is introduced. According to the two, the pixel is divided into strong, medium and weak texture regions, and then different thresholds are assigned to the pixels in different regions. Then the image is converted from the RGB color space to the HSV color space and the matching window is dynamically generated according to the region threshold and color threshold. The HAD cost calculation function was established and the traditional Census algorithm was improved. The disparity map was obtained by nonlinear fusion calculation, and the obtained disparity map was detected by subpixel and filtered by median value. Finally, the high-precision disparity map was obtained. Experimental results show that the proposed algorithm is effective, has high matching accuracy, and has good robustness to optical distortion and edge information conditions.