Name
Title | ||
|---|---|---|
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows |
Abstract | ||
|---|---|---|
The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications. Current strategies aim to synthesize realistic training data by modeling noise and degradations that appear in real-world settings. We propose DeFlow, a method for learning stochastic image degradations from unpaired data. Our approach is based on a ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CVPR46437.2021.00016 | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Keywords | DocType | ISSN |
Degradation,Superresolution,Training data,Stochastic processes,Data models,Image restoration,Pattern recognition | Conference | 1063-6919 |
ISBN | Citations | PageRank |
978-1-6654-4509-2 | 2 | 0.36 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valentin Wolf | 1 | 2 | 0.36 |
| Andreas Lugmayr | 2 | 2 | 1.04 |
| Danelljan Martin | 3 | 1344 | 49.35 |
| Luc Van Gool | 4 | 27566 | 1819.51 |
| Radu Timofte | 5 | 1880 | 118.45 |