Summary of Spatial-and-frequency-aware Restoration Method For Images Based on Diffusion Models, by Kyungsung Lee et al.
Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models
by Kyungsung Lee, Donggyu Lee, Myungjoo Kang
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes a novel diffusion model called SaFaRI for image restoration (IR) with Gaussian noise. The model builds upon the success of diffusion models in producing high-quality reconstructions while leveraging established methods in IR. Unlike existing approaches that focus on pixel-wise data-fidelity, SaFaRI encourages images to preserve data-fidelity in both spatial and frequency domains. This leads to enhanced reconstruction quality. The authors evaluate their model’s performance on various noisy inverse problems, including inpainting, denoising, and super-resolution. Results demonstrate state-of-the-art performance on ImageNet and FFHQ datasets using LPIPS and FID metrics, outperforming existing zero-shot IR methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to fix noisy images. It uses something called diffusion models, which are good at making pictures look clear again. The old ways of fixing noisy images only looked at each pixel separately, but this new method looks at the whole image and its patterns too. This makes it better at restoring the original picture. The authors tested their method on many types of noisy problems, like filling in missing parts or removing noise from an image. It worked really well and was even better than other methods that didn’t need any training data! |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Super resolution * Zero shot
