Efficient MCMC-based image deblurring with Neumann boundary conditions

zbMath1288.65002MaRDI QIDQ2450064

James G. Nagy, Marylesa Howard, Johnathan M. Bardsley

Publication date: 14 May 2014

Published in: ETNA. Electronic Transactions on Numerical Analysis (Search for Journal in Brave)

Full work available at URL: http://www.emis.de/journals/ETNA/volumes/2011-2020/vol40/abstract_vol40_pp476-488.html

zbMATH Keywords

inverse problemsnumerical experimentsNeumann boundary conditionsBayesian inferenceMarkov chain Monte Carlo methodsdiscrete cosine transformimage deblurringGaussian Markov random fieldsill-posed deconvolution problemssymmetric convolution kernel

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Numerical analysis or methods applied to Markov chains (65C40) Integral equations of the convolution type (Abel, Picard, Toeplitz and Wiener-Hopf type) (45E10) Inverse problems for integral equations (45Q05) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items (2)

Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems ⋮ Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems

Uses Software

• GMRFLib
• BayesDA

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