Papers › PromptIR: Prompting for All-in-One Blind Image Restoration
PromptIR: Prompting for All-in-One Blind Image Restoration
Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan
Image restoration involves recovering a high-quality clean image from its degraded version. Deep learning-based methods have significantly improved image restoration performance, however, they have limited generalization ability to different degradation types and levels. This restricts their real-world application since it requires training individual models for each specific degradation and knowing the input degradation type to apply the relevant model. We present a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation. In particular, our method uses prompts to encode degradation-specific information, which is then used to dynamically guide the restoration network. This allows our method to generalize to different degradation types and levels, while still achieving state-of-the-art results on image denoising, deraining, and dehazing. Overall, PromptIR offers a generic and efficient plugin module with few lightweight prompts that can be used to restore images of various types and levels of degradation with no prior information on the corruptions present in the image. Our code and pretrained models are available here: https://github.com/va1shn9v/PromptIR
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Blind All-in-One Image Restoration | 3-Degradations | PromptIR | Average PSNR | 32.06 | #7 of 9 | Archive leaderboard | report |
| Blind All-in-One Image Restoration | 3-Degradations | PromptIR | SSIM | 0.913 | #7 of 9 | Archive leaderboard | report |
| Image Restoration | CDD-11 | PromptIR | Average PSNR (dB) | 25.90 | #8 of 14 | Archive leaderboard | report |
| Image Restoration | CDD-11 | PromptIR | SSIM | 0.8499 | #8 of 14 | 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.
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