Summary of Blind Inverse Problem Solving Made Easy by Text-to-image Latent Diffusion, By Michail Dontas et al.
Blind Inverse Problem Solving Made Easy by Text-to-Image Latent Diffusion
by Michail Dontas, Yutong He, Naoki Murata, Yuki Mitsufuji, J. Zico Kolter, Ruslan Salakhutdinov
First submitted to arxiv on: 30 Nov 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, LADiBI, tackles blind inverse problems in computer vision by employing large-scale text-to-image diffusion models. Without relying on restrictive assumptions like additional training or operator linearity, LADiBI jointly models priors for both the target image and operator using natural language prompts. This flexibility allows it to adapt across various tasks. A novel posterior sampling approach is also introduced, combining effective operator initialization with iterative refinement, enabling LADiBI to operate without predefined operator forms. Experimental results demonstrate LADiBI’s capabilities in solving a range of image restoration tasks, including linear and nonlinear problems on diverse target image distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LADiBI is a new way to solve blind inverse problems in computer vision. These problems are hard because we don’t know what the data looks like or how it was created. Current methods make assumptions that limit their use. LADiBI uses big text-to-image models to find solutions without making those assumptions. It’s flexible and can work on different tasks. The paper also introduces a new way to improve the model by refining its guesses about the operator. The results show that LADiBI is good at solving many types of image restoration problems. |
Keywords
» Artificial intelligence » Diffusion
