Papers › Restore Anything Model via Efficient Degradation Adaptation
Restore Anything Model via Efficient Degradation Adaptation
Bin Ren, Eduard Zamfir, Zongwei Wu, Yawei Li, Yidi Li, Danda Pani Paudel, Radu Timofte, Ming-Hsuan Yang, Nicu Sebe
With the proliferation of mobile devices, the need for an efficient model to restore any degraded image has become increasingly significant and impactful. Traditional approaches typically involve training dedicated models for each specific degradation, resulting in inefficiency and redundancy. More recent solutions either introduce additional modules to learn visual prompts significantly increasing model size or incorporate cross-modal transfer from large language models trained on vast datasets, adding complexity to the system architecture. In contrast, our approach, termed RAM, takes a unified path that leverages inherent similarities across various degradations to enable both efficient and comprehensive restoration through a joint embedding mechanism without scaling up the model or relying on large multimodal models. Specifically, we examine the sub-latent space of each input, identifying key components and reweighting them in a gated manner. This intrinsic degradation awareness is further combined with contextualized attention in an X-shaped framework, enhancing local-global interactions. Extensive benchmarking in an all-in-one restoration setting confirms RAM's SOTA performance, reducing model complexity by approximately 82% in trainable parameters and 85% in FLOPs. Our code and models will be publicly available.
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 | AnyIR | Average PSNR | 29.65 | #5 of 7 | Archive leaderboard | report |
| Blind All-in-One Image Restoration | 3-Degradations | RAM | Average PSNR | 32.51 | #6 of 9 | Archive leaderboard | report |
| Blind All-in-One Image Restoration | 3-Degradations | RAM | SSIM | 0.916 | #6 of 9 | Archive leaderboard | report |
| Blind All-in-One Image Restoration | 5-Degradations | AnyIR | Average PSNR | 29.65 | #7 of 9 | Archive leaderboard | report |
| Blind All-in-One Image Restoration | 5-Degradations | AnyIR | SSIM | 0.901 | #7 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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