Bayesian Formulation of Regularization by Denoising – Model and Monte Carlo Sampling
Image restoration aims at recovering a clean image from degraded observations. This paper presents a novel Bayesian framework for image restoration using a regularization-by-denoising (RED) prior. It introduces a probabilistic counterpart to the RED paradigm, and proposes a new Monte Carlo algorithm to efficiently sample from the resulting posterior distribution. The proposed method benefit from the recent developments of deep learning-based denoisers. Extensive numerical experiments illustrate the efficiency of the proposed method, showcasing its competitive performance against state-of-the-art methods.
Authors
- Mame Diarra Fall (ORCID: https://orcid.org/0000-0003-2749-2589)
- Nicolas Dobigeon (ORCID: https://orcid.org/0000-0001-8127-350X)
- Elhadji C. Faye (ORCID: https://orcid.org/0009-0006-2969-1433)
- Aladine Chetouani
Institutions
- Université Toulouse III - Paul Sabatier (FR)
- Institut National Polytechnique de Toulouse (FR)
- Université Toulouse-I-Capitole (FR)
- Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique (FR)
- Institut de Recherche en Informatique de Toulouse (FR)
- Université Toulouse - Jean Jaurès (FR)
- Institut Polytechnique de Bordeaux (FR)
- École Nationale Supérieure d'Électrotechnique, d'Électronique, d'Informatique, d'Hydraulique et des Télécommunications (FR)
- Institut Denis Poisson (FR)
Publication Details
- Published
- 2024-10-02
- DOI
- https://doi.org/10.1109/mmsp61759.2024.10743289
- Primary Topic
- Image and Signal Denoising Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00