Summary of Listening to the Noise: Blind Denoising with Gibbs Diffusion, by David Heurtel-depeiges et al.
Listening to the Noise: Blind Denoising with Gibbs Diffusion
by David Heurtel-Depeiges, Charles C. Margossian, Ruben Ohana, Bruno Régaldo-Saint Blancard
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Cosmology and Nongalactic Astrophysics (astro-ph.CO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Signal Processing (eess.SP)
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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 introduces Gibbs Diffusion (GDiff), a novel methodology for blind denoising that addresses the limitations of diffusion-based posterior sampling. By alternating between conditional diffusion models and Monte Carlo samplers, GDiff can infer both signal and noise parameters from arbitrary parametric Gaussian noise distributions. Theoretical analysis highlights potential pitfalls, guides diagnostic usage, and quantifies errors in the Gibbs stationary distribution caused by the diffusion model. Applications showcased include blind denoising of natural images involving colored noises with unknown amplitude and spectral index, as well as a cosmology problem analyzing cosmic microwave background data to constrain models of the Universe’s evolution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to remove noise from blurry images or data without knowing what kind of noise it is. The method uses a combination of computer algorithms and statistical techniques to figure out both the original image and the type of noise that was added. This can be useful for tasks like analyzing old photographs or astronomical data, where we want to get the best possible picture of what’s going on. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model
