Name
Abstract | ||
|---|---|---|
In recent years, deep learning-based methods have been successfully applied to the image distortion restoration tasks. However, scenarios that assume a single distortion only may not be suitable for many real-world applications. To deal with such cases, some studies have proposed sequentially combined distortions datasets. Viewing in a different point of combining, we introduce a spatially-heterogeneous distortion dataset in which multiple corruptions are applied to the different locations of each image. In addition, we also propose a mixture of experts network to effectively restore a multi-distortion image. Motivated by the multi-task learning, we design our network to have multiple paths that learn both common and distortion-specific representations. Our model is effective for restoring real-world distortions and we experimentally verify that our method outperforms other models designed to manage both single distortion and multiple distortions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-69532-3_12 | ACCV (2) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sijin Kim | 1 | 0 | 0.34 |
| Namhyuk Ahn | 2 | 6 | 3.13 |
| Kyung-ah Sohn | 3 | 116 | 10.98 |