Closed-form Bayesian image denoising: improving the adaptive Wiener filter through pairwise Gaussian-Markov random fields

DOI10.1080/03610918.2019.1577969zbMath1489.62301OpenAlexW2931460948WikidataQ128105346 ScholiaQ128105346MaRDI QIDQ5082593

Alexandre L. M. Levada

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1577969

zbMATH Keywords

Bayesian estimationimage denoisingmaximum pseudo-likelihood estimationGaussian-Markov random fieldadaptive Wiener filter

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Pattern recognition, speech recognition (68T10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

• Unnamed Item
• Unnamed Item
• Optimization by Simulated Annealing
• Information geometry, simulation and complexity in Gaussian random fields
• Edge preserving image denoising with a closed form solution
• Image denoising in steerable pyramid domain based on a local Laplace prior
• Probabilistic Solution of Ill-Posed Problems in Computational Vision
• Alternatives to the Median Absolute Deviation
• Is Denoising Dead?
• A Review of Image Denoising Algorithms, with a New One
• Pixon-based image denoising with Markov random fields

This page was built for publication: Closed-form Bayesian image denoising: improving the adaptive Wiener filter through pairwise Gaussian-Markov random fields