Spatially adaptive Bayesian image reconstruction through locally-modulated Markov random field models

DOI10.1214/18-BJPS399zbMath1493.62554OpenAlexW2801421298WikidataQ127734802 ScholiaQ127734802MaRDI QIDQ2318627

Robert G. Aykroyd, Salem M. Al-Gezeri

Publication date: 15 August 2019

Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.bjps/1560153849

zbMATH Keywords

inverse problemsMarkov chain Monte Carloimage reconstructionBayesian modelprior modelsmagnetometry

Mathematics Subject Classification ID

Random fields; image analysis (62M40)

Uses Software

• R

Cites Work

• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Penalized Pseudolikelihood Inference in Spatial Interaction Models with Covariates
• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• Handbook of Markov Chain Monte Carlo
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Global and local priors, and the location of lesions using gamma-camera imagery
• Markov random fields for texture classification
• Fully Bayesian Reconstructions From Single-Photon Emission Computed Tomography Data
• Inhomogeneous Prior Models for Image Reconstruction
• Bayesian Methods Applied to Survey Data From Archeological Magnetometry
• Monte Carlo strategies in scientific computing
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