Summary of Learning Image Priors Through Patch-based Diffusion Models For Solving Inverse Problems, by Jason Hu et al.
Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problems
by Jason Hu, Bowen Song, Xiaojian Xu, Liyue Shen, Jeffrey A. Fessler
First submitted to arxiv on: 4 Jun 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 The paper proposes a method to learn efficient data priors for high-dimensional and high-resolution data like 3D images using diffusion models. The existing methods are computationally expensive and require large amounts of data. The authors introduce a patch-based position-aware diffusion inverse solver, called PaDIS, which learns the score function of the whole image by combining scores of patches and their positional encoding. This approach achieves improved memory efficiency, data efficiency, and flexibility to be used with different diffusion inverse solvers. The proposed method is demonstrated to solve various inverse problems in natural and medical image domains, including CT reconstruction, deblurring, and superresolution, even when trained on limited patch-based priors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better from images by using a new way of processing data called diffusion models. These models are good at learning patterns in pictures but can be slow to use. The authors came up with a clever idea to break down the image into smaller pieces, learn patterns in each piece, and then combine them to create a complete picture. This makes their method more efficient and flexible than before. They tested it on different kinds of images and problems, like reconstructing X-rays and making blurry pictures clear again. Their results show that this new approach can be very useful even when we don’t have as much data as we would like. |
Keywords
» Artificial intelligence » Diffusion » Positional encoding
