Improving Stereo Matching Accuracy with Blind Super-Resolution Networks

Due to the limitation of the camera's photoreceptor, the stereo images obtained from stereo cameras can only be presented in the form of pixel blocks, which cannot show the more minute object information in sub-pixel units between the pixel blocks, which may restricts the breakthrough of stereo matching in sub-pixel accuracy. For this reason, we innovatively use a blind super-resolution network to simulate the sub-pixel effect and improve the accuracy of stereo matching. We combine the super-resolution network with the stereo matching and design a new stereo matching process: the stereo image is first zoomed in by the blind super-resolution network to supplement the local information, and then the high-resolution image is inputted into the stereo matching algorithm to generate a dense and fine disparity map. Through experiments, we find that the blind super-resolution network BSRGAN can effectively improve the stereo matching accuracy in most scenarios. However, when facing repetitive and dense texture regions, the limitation of blind super-resolution network leads to a degradation of the matching accuracy. Nevertheless, our study provides a new idea and method for improving stereo matching accuracy.