Name
Abstract | ||
|---|---|---|
•A brightness-adaptive kernel prediction network is proposed for iTM task.•Restoration of details in underexposed regions is important but difficult.•Region loss forces the model pay attention to overexposed and underexposed regions.•Our method achieves the best performance compared with recent competitors. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.08.057 | Neurocomputing |
Keywords | DocType | Volume |
HDR image,Inverse tone mapping,Kernel prediction network,Brightness-adaptive skip connection | Journal | 464 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaofeng Cao | 1 | 0 | 0.68 |
| Zhou | 2 | 78 | 11.31 |
| Kanglin Liu | 3 | 0 | 1.69 |
| Bozhi Liu | 4 | 19 | 9.43 |