Name
Title | ||
|---|---|---|
Depth Map Super-Resolution Via Multilevel Recursive Guidance And Progressive Supervision |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2914065 | IEEE ACCESS |
Keywords | Field | DocType |
Depth map, super-resolution, multilevel recursion guidance, progressive supervision, residual fusion | Residual,Computer vision,Computer science,Artificial intelligence,Deep learning,Depth map,Fuse (electrical),Superresolution,Recursion,Distributed computing,Color image | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bolan Yang | 1 | 0 | 1.01 |
| Xiaoting Fan | 2 | 12 | 3.24 |
| Zexun Zheng | 3 | 0 | 0.68 |
| Xiaohuan Liu | 4 | 0 | 1.01 |
| Kaiming Zhang | 5 | 0 | 0.34 |
| Jianjun Lei | 6 | 713 | 52.69 |