Stereo matching algorithm based on improved Census transform and texture filtering

To solve the problem that existing binocular stereo matching algorithms have low matching accuracy in discontinuous disparity and low texture area, a stereo matching algorithm based on Census transform and texture filtering is proposed. The weighted Census transform circular template is used to carry out the matching cost, which reflects the influence of the distance between neighborhood pixels and target pixels on the calculation and expands the perception range of target pixels; the texture filtering method is used for cost aggregation, which highlights the image structure information and smooths the internal texture. The experimental results show that the stereo matching algorithm proposed in this paper can effectively reduce the mismatching rate of images, the disparity map obtained has less noise, and the matching effect is better when the pattern texture is relatively dense.

Automated summary

A stereo matching algorithm based on Census transform and texture filtering is proposed in this paper to solve the problem of low matching accuracy in discontinuous disparity and low texture area for existing binocular stereo matching algorithms. The algorithm uses the weighted Census transform circular template for matching cost calculation, which reflects the influence of the distance between neighborhood pixels and target pixels and expands the perception range of target pixels. The texture filtering method is used for cost aggregation to highlight the image structure information and smooth the internal texture. Experimental results show that the proposed algorithm effectively reduces the mismatching rate of images, produces disparity maps with less noise, and achieves better matching effect in relatively dense pattern textures.