Symmetric segment-based stereo matching of motion blurred images with illumination variations

Most existing methods of stereo matching focus on dealing with clear image pairs. Consequently, there is a lack of approaches capable of handling degraded images captured under challenging real situations, e.g. motion blur is present and an image pair is in different illumination conditions. In this paper we propose a novel approach to handling these challenging situations by formulating the problem into a Maximum a Posteriori (MAP) estimation framework, and adopt a segment-based symmetric stereo matching method to infer a mask of disparity map which indicates whether a disparity is affected by motion blur and estimate the disparity value. The experimental results show that our stereo matching method is able to compute more accurate disparity maps of this type of degraded images.