Summary of Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data, by Asad Aali and Giannis Daras et al.
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models trained on Corrupted Data
by Asad Aali, Giannis Daras, Brett Levac, Sidharth Kumar, Alexandros G. Dimakis, Jonathan I. Tamir
First submitted to arxiv on: 13 Mar 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 The proposed framework, Ambient Diffusion Posterior Sampling (A-DPS), leverages a pre-trained generative model to solve inverse problems with diffusion models learned from linearly corrupted data. The method uses posterior sampling conditioned on measurements from different forward processes, demonstrating improved performance and speed for image restoration tasks. The authors test their approach on standard natural image datasets and show that A-DPS can outperform models trained on clean data in certain scenarios. A-DPS is further extended to train MRI models using Fourier subsampled multi-coil MRI measurements at various acceleration factors, revealing better priors for solving inverse problems in the high acceleration regime. The authors open-source their code and trained Ambient Diffusion MRI models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Inverse problems are solved using a new framework called Ambient Diffusion Posterior Sampling (A-DPS). This method helps diffusion models learn from linearly corrupted data to solve problems like restoring blurry images or incomplete medical scans. A-DPS is tested on real images and shows that it can sometimes do better than models trained on perfect data. This technique is also used to train MRI machines to work with incomplete data, which is useful for speeding up medical imaging processes. The code and results are shared openly so others can use this method too. |
Keywords
* Artificial intelligence * Diffusion * Generative model
