Depth Map Super-Resolution via Multilevel Recursive Guidance and Progressive Supervision

With the development of deep learning, image super-resolution has made great breakthroughs. However, compared with a color image, the performance of depth map super-resolution is still poor. To address this problem, multilevel recursive guidance and progressive supervised network (MRG-PS) is proposed in this paper. First, a multilevel recursive guidance architecture is presented to extract features of a color stream and depth stream, in which the depth stream is guided by the color features at each level. Second, a progressive supervision module is developed to supervise the multilevel recursion to obtain depth residual information on different levels. Finally, a residual fusion and construction strategy is designed to fuse all residual information and reconstruct the high-resolution depth map. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods.