Point Spread Function Estimation in X-Ray Imaging with Partially Collapsed Gibbs Sampling

DOI10.1137/17M1149250zbMath1387.65005arXiv1709.09105OpenAlexW2963870631WikidataQ59940748 ScholiaQ59940748MaRDI QIDQ4641603

Kevin T. Joyce, Aaron Luttman, Johnathan M. Bardsley

Publication date: 18 May 2018

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1709.09105

zbMATH Keywords

inverse problemsBayesian inferenceMarkov chain Monte Carlo methodsuncertainty quantificationcomputational imaging

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Computing methodologies for image processing (68U10) Biomedical imaging and signal processing (92C55) Numerical analysis or methods applied to Markov chains (65C40) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (3)

A blocking scheme for dimension-robust Gibbs sampling in large-scale image deblurring ⋮ Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems ⋮ Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems

Uses Software

• GMRFLib
• BayesDA

Cites Work

• Sampling-based uncertainty quantification in deconvolution of X-ray radiographs
• Statistical and computational inverse problems.
• Bayesian Abel Inversion in Quantitative X-Ray Radiography
• Inverse problems: A Bayesian perspective
• MCMC-Based Image Reconstruction with Uncertainty Quantification
• Analysis of the Gibbs Sampler for Hierarchical Inverse Problems
• Fast Sampling in a Linear-Gaussian Inverse Problem
• Deblurring Images
• Partially Collapsed Gibbs Samplers
• A simple method for generating gamma variables
• Numerical Solution of Partial Differential Equations
• Gaussian Markov Random Fields
• Computational Methods for Inverse Problems
• A METROPOLIS-HASTINGS METHOD FOR LINEAR INVERSE PROBLEMS WITH POISSON LIKELIHOOD AND GAUSSIAN PRIOR
• Discrete Inverse Problems
• Introduction to Bayesian Scientific Computing
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