Summary of Regularization by Denoising: Bayesian Model and Langevin-within-split Gibbs Sampling, By Elhadji C. Faye et al.
Regularization by denoising: Bayesian model and Langevin-within-split Gibbs sampling
by Elhadji C. Faye, Mame Diarra Fall, Nicolas Dobigeon
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper develops a Bayesian approach for image inversion, building upon the regularization-by-denoising (RED) paradigm. It introduces a Monte Carlo algorithm tailored for sampling from the resulting posterior distribution, based on an asymptotically exact data augmentation (AXDA). The method combines split Gibbs sampling (SGS) with one Langevin Monte Carlo step, showcasing its effectiveness in various imaging tasks such as deblurring, inpainting, and super-resolution. Through extensive numerical experiments, this framework demonstrates its efficacy for Bayesian inference in imaging, leveraging data-driven regularization strategies within a probabilistic framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to use special math tools (Bayesian approach) to fix blurry or missing parts in images. It uses a clever way of combining old and new information to make the image clearer. The method is tested on different tasks like removing blur, filling gaps, and making high-resolution pictures from low-quality ones. The results show that this method works well for these tasks. |
Keywords
* Artificial intelligence * Bayesian inference * Data augmentation * Regularization * Super resolution
