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

IEEE Access

Xiaohuan Liu¹  Jianjun Lei¹  Xiaoting Fan¹  Bolan Yang¹  Zexun Zheng¹  Kaiming Zhang² 

[1] School of Electrical and Information Engineering, Tianjin University, Tianjin, China;Tianjin International Engineering Institute, Tianjin University, Tianjin, China;

关键词: Depth map;    super-resolution;    multilevel recursion guidance;    progressive supervision;    residual fusion;   

DOI  :  10.1109/ACCESS.2019.2914065

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown