Sampling-based uncertainty quantification in deconvolution of X-ray radiographs

DOI10.1016/j.cam.2014.02.024zbMath1321.65031OpenAlexW2024966774MaRDI QIDQ491576

Michael Fowler, Aaron Luttman, Marylesa Howard

Publication date: 26 August 2015

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2014.02.024

zbMATH Keywords

algorithmtotal variationinverse problemsMarkov chain Monte Carlodeconvolutionimage analysisregularization methodsuncertainty quantificationPoisson likelihoodsingle reconstructionX-ray radiography

Mathematics Subject Classification ID

Image analysis in multivariate analysis (62H35) Monte Carlo methods (65C05) Biomedical imaging and signal processing (92C55) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Radon transform (44A12) Numerical analysis or methods applied to Markov chains (65C40) Numerical methods for integral transforms (65R10) Numerical methods for inverse problems for integral equations (65R32)

Related Items (3)

A blocking scheme for dimension-robust Gibbs sampling in large-scale image deblurring ⋮ Point Spread Function Estimation in X-Ray Imaging with Partially Collapsed Gibbs Sampling ⋮ Bayesian Abel Inversion in Quantitative X-Ray Radiography

Uses Software

• BayesDA

Cites Work

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