Name
Abstract | ||
|---|---|---|
The spatio-temporal information among video sequences is significant for video super-resolution (SR). However, the spatio-temporal information cannot be fully used by existing video SR methods since spatial feature extraction and temporal motion compensation are usually performed sequentially. In this paper, we propose a deformable 3D convolution network (D3Dnet) to incorporate spatio-temporal information from both spatial and temporal dimensions for video SR. Specifically, we introduce deformable 3D convolution (D3D) to integrate deformable convolution with 3D convolution, obtaining both superior spatio-temporal modeling capability and motion-aware modeling flexibility. Extensive experiments have demonstrated the effectiveness of D3D in exploiting spatio-temporal information. Comparative results show that our network achieves state-of-the-art SR performance. Code is available at: https://github.com/XinyiYing/D3Dnet. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LSP.2020.3013518 | IEEE SIGNAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Convolution, Three-dimensional displays, Motion compensation, Feature extraction, Image resolution, Signal resolution, Solid modeling, Video super-resolution, deformable convolution | Journal | 27 |
ISSN | Citations | PageRank |
1070-9908 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyi Ying | 1 | 7 | 1.48 |
| Longguang Wang | 2 | 24 | 7.53 |
| Yingqian Wang | 3 | 149 | 26.17 |
| Weidong Sheng | 4 | 4 | 0.75 |
| wei an | 5 | 62 | 17.06 |
| Yulan Guo | 6 | 672 | 50.74 |