Name
Abstract | ||
|---|---|---|
•Hierarchical dense connection provides rich linking ways for feature representation.•Reasonable design and parameter sharing contribute for light-weight SR model.•Global feature fusion promotes fine residual learning. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.patcog.2020.107475 | Pattern Recognition |
Keywords | DocType | Volume |
Super-resolution,Hierarchical dense connection,Global feature fusion,Recursive network,Multi-scale | Journal | 107 |
Issue | ISSN | Citations |
1 | 0031-3203 | 8 |
PageRank | References | Authors |
0.53 | 27 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kui Jiang | 1 | 94 | 17.91 |
| Zhongyuan Wang | 2 | 227 | 25.14 |
| Peng Yi | 3 | 36 | 12.92 |
| Junjun Jiang | 4 | 1138 | 74.49 |