Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights
Institut Mines-Télécom · Laboratoire Traitement et Communication de l’Information · +5 more institutions
Abstract
Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the nonlocal means (NL means) algorithm introduced by Buades , which performs a weighted average of the values of similar pixels. Pixel similarity is defined in NL means as the Euclidean distance between patches (rectangular windows centered on each two pixels). In this paper, a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model. The denoising process is expressed as a weighted…
Citation impact
- FWCI
- 24.95
- Percentile
- 100%
- References
- 61
Authors
3- CDCharles‐Alban DeledalleCorresponding
Institut Mines-Télécom, Laboratoire Traitement et Communication de l’Information, Télécom Paris, Centre National de la Recherche Scientifique
- LDLaurent Denis
Laboratoire Hubert Curien, Centre National de la Recherche Scientifique, École d'Ingénieurs en Chimie et Sciences du Numérique, École Normale Supérieure - PSL
- FTFlorence Tupin
Télécom Paris, Institut Mines-Télécom, Centre National de la Recherche Scientifique, Laboratoire Traitement et Communication de l’Information
Topics & keywords
- Noise reduction
- Artificial intelligence
- Non-local means
- Pattern recognition (psychology)
- Mathematics
- Noise (video)
- Pixel
- Similarity (geometry)
- Sustainable cities and communities