Summary of Solving Inverse Problems Via Diffusion Optimal Control, by Henry Li et al.
Solving Inverse Problems via Diffusion Optimal Control
by Henry Li, Marcus Pereira
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 for diffusion-based inverse problem solvers reframes the signal recovery task as a discrete optimal control episode, sidestepping limitations of existing approaches. The new framework is fully general, handling differentiable forward measurement operators, and recovers the idealized posterior sampling equation as a special case. It outperforms neural inverse problem solvers in image reconstruction with inverse problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to solve inverse problems using diffusion-based methods. Instead of treating it like a probabilistic sampling episode, it’s an optimal control problem that can be solved more easily and accurately. This approach works for many types of inverse problems, including super-resolution and deblurring. It even beats some neural networks at image reconstruction. |
Keywords
» Artificial intelligence » Diffusion » Super resolution
