Summary of Instantir: Blind Image Restoration with Instant Generative Reference, by Jen-yuan Huang et al.
InstantIR: Blind Image Restoration with Instant Generative Reference
by Jen-Yuan Huang, Haofan Wang, Qixun Wang, Xu Bai, Hao Ai, Peng Xing, Jen-Tse Huang
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel diffusion-based Blind Image Restoration (BIR) method called Instant-reference Image Restoration (InstantIR) is introduced to tackle test-time unknown degradation in BIR tasks. This method dynamically adjusts generation conditions during inference by leveraging prior knowledge, either from human input or generative models. The approach involves extracting a compact representation of the input using a pre-trained vision encoder and decoding the current diffusion latent to instantiate it in the generative prior. The degraded image is then encoded with this reference, providing a robust generation condition. Furthermore, the variance of generative references is observed to fluctuate with degradation intensity, which is leveraged as an indicator for developing a sampling algorithm adaptive to input quality. InstantIR achieves state-of-the-art performance and outstanding visual quality in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to restore blurry or damaged images called InstantIR has been developed. It uses prior knowledge, like what humans know or what computer models can do, to help improve the image restoration process. The method works by first getting a good representation of the original image using a special kind of computer model. Then, it uses this representation to generate new details in the restored image. The method is very good at restoring images and also allows for creative restoration, where the algorithm can make artistic choices about what to restore. |
Keywords
» Artificial intelligence » Diffusion » Encoder » Inference
