Papers › Ingredient-Oriented Multi-Degradation Learning for Image Restoration
Ingredient-Oriented Multi-Degradation Learning for Image Restoration
Jinghao Zhang, Jie Huang, Mingde Yao, Zizheng Yang, Hu Yu, Man Zhou, Feng Zhao
Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation. Recent years have witnessed the flourish of various All-in-one methods, which handle multiple image degradations within a single model. In practice, however, few attempts have been made to excavate task correlations in that exploring the underlying fundamental ingredients of various image degradations, resulting in poor scalability as more tasks are involved. In this paper, we propose a novel perspective to delve into the degradation via an ingredients-oriented rather than previous task-oriented manner for scalable learning. Specifically, our method, named Ingredients-oriented Degradation Reformulation framework (IDR), consists of two stages, namely task-oriented knowledge collection and ingredients-oriented knowledge integration. In the first stage, we conduct ad hoc operations on different degradations according to the underlying physics principles, and establish the corresponding prior hubs for each type of degradation. While the second stage progressively reformulates the preceding task-oriented hubs into single ingredients-oriented hub via learnable Principal Component Analysis (PCA), and employs a dynamic routing mechanism for probabilistic unknown degradation removal. Extensive experiments on various image restoration tasks demonstrate the effectiveness and scalability of our method. More importantly, our IDR exhibits the favorable generalization ability to unknown downstream tasks.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 5-Degradation Blind All-in-One Image Restoration | 5-Degradation Blind All-in-One Image Restoration | IDR | Average PSNR | 28.34 | #6 of 7 | Archive leaderboard | report |
| Blind All-in-One Image Restoration | 5-Degradations | IDR | Average PSNR | 28.34 | #8 of 9 | Archive leaderboard | report |
| Blind All-in-One Image Restoration | 5-Degradations | IDR | SSIM | 0.893 | #8 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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