Edge-Guided Depth Image Super-Resolution Based on KSVD

This paper proposes an edge-guided super-resolution algorithm for single frame depth images based on K singular value decomposition (KSVD). Compared with conventional algorithms, the proposed algorithm has two key contributions. Firstly it suppresses the jagged edge effect of up-sampled depth images by KSVD, which learns a complete dictionary to describe the mapping between the jagged edges and corresponding smooth ones. Secondly it improves the joint bilateral filter based on connectivity. The improved filter can not only preserve the sharpness of the edges during the interpolation, but also suppress noise. The proposed algorithm has been extensively tested on the Middlebury dataset and compared with some existing state-of-the-art methods. Both quantitative and qualitative experimental results show its performance superiority.