Summary of Chain-of-restoration: Multi-task Image Restoration Models Are Zero-shot Step-by-step Universal Image Restorers, by Jin Cao et al.
Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image Restorers
by Jin Cao, Deyu Meng, Xiangyong Cao
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to image restoration, addressing the issue of composite degradations. The existing methods focus on isolated degradations, but recent research has shifted towards tackling complex combinations of multiple degradations. However, building training data for these composite degradations becomes increasingly burdensome. To alleviate this issue, the paper introduces Universal Image Restoration (UIR), which requires training only on a set of degradation bases and then removing any degradation that these bases can potentially compose in a zero-shot manner. The authors draw inspiration from the Chain-of-Thought mechanism used by large language models to propose the Chain-of-Restoration (CoR) mechanism, which instructs models to remove unknown composite degradations step-by-step. By integrating a simple Degradation Discriminator into pre-trained multi-task models, CoR facilitates the process of removing one degradation basis per step until the image is fully restored from the unknown composite degradation. The paper demonstrates that CoR can significantly improve model performance in removing composite degradations, achieving comparable or better results than state-of-the-art methods trained on all degradations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make images look better when they’re messed up. Right now, computer programs can fix some simple problems, like making a blurry picture clearer. But what if the problem is more complicated, like fixing an image that’s both blurry and has weird colors? Most programs need to learn how to fix all kinds of problems separately, which is hard and time-consuming. The authors of this paper came up with a new way to teach computers to fix these complex problems without needing to learn about each one individually. They called it Universal Image Restoration (UIR). It works by teaching the computer to remove small parts of the problem at a time, like taking away the bluriness and then the weird colors. This new method is better than other methods that have been tried before. |
Keywords
» Artificial intelligence » Multi task » Zero shot
