A depth map post-processing approach based on adaptive random walk with restart
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Javidnia, Hossein; Corcoran, Peter (2016). A depth map post-processing approach based on adaptive random walk with restart. IEEE Access 4 , 5509-5519
Accurate depth estimation is still an important challenge after a decade, particularly from stereo images. The accuracy comes from a good depth level and preserved structure. For this purpose, a depth post-processing framework is proposed in this paper. The framework starts with the &quot;Adaptive Random Walk with Restart (2015)'' algorithm. To refine the depth map generated by this method, we introduced a form of median solver/filter based on the concept of the mutual structure, which refers to the structural information in both images. This filter is further enhanced by a joint filter. Next, a transformation in image domain is introduced to remove the artifacts that cause distortion in the image. The proposed post-processing method is then compared with the top eight algorithms in the Middlebury benchmark. To explore how well this method is able to compete with more widely known techniques, a comparison is performed with Google's new depth map estimation method. The experimental results demonstrate the accuracy and efficiency of the proposed post-processing method.