MCMC-Based Image Reconstruction with Uncertainty Quantification
From MaRDI portal
Publication:2909266
DOI10.1137/11085760XzbMath1246.15022OpenAlexW2062943478MaRDI QIDQ2909266
Publication date: 30 August 2012
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/11085760x
Bayesian inference (62F15) Ill-posedness and regularization problems in numerical linear algebra (65F22) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Inverse problems in linear algebra (15A29)
Related Items (35)
A Krylov subspace type method for Electrical Impedance Tomography ⋮ Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format ⋮ Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification ⋮ Sampling Strategies for Fast Updating of Gaussian Markov Random Fields ⋮ Fast Sampling in a Linear-Gaussian Inverse Problem ⋮ Estimating airborne particulate emissions using a finite-volume forward solver coupled with a Bayesian inversion approach ⋮ Fitting large-scale structured additive regression models using Krylov subspace methods ⋮ Sampling hyperparameters in hierarchical models: Improving on Gibbs for high-dimensional latent fields and large datasets ⋮ Bayesian optical flow with uncertainty quantification ⋮ Empirical Bayesian Inference Using a Support Informed Prior ⋮ Sub-aperture SAR imaging with uncertainty quantification ⋮ Generalized Sparse Bayesian Learning and Application to Image Reconstruction ⋮ Sequential edge detection using joint hierarchical Bayesian learning ⋮ Large-Scale Bayesian Spatial-Temporal Regression with Application to Cardiac MR-Perfusion Imaging ⋮ Sequential image recovery using joint hierarchical Bayesian learning ⋮ MCMC Algorithms for Computational UQ of Nonnegativity Constrained Linear Inverse Problems ⋮ An extended Perona-Malik model based on probabilistic models ⋮ A Bayesian linear model for the high-dimensional inverse problem of seismic tomography ⋮ Localization for MCMC: sampling high-dimensional posterior distributions with local structure ⋮ Sampling-based uncertainty quantification in deconvolution of X-ray radiographs ⋮ Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems ⋮ Point Spread Function Estimation in X-Ray Imaging with Partially Collapsed Gibbs Sampling ⋮ Bayesian inversion for anisotropic hydraulic phase-field fracture ⋮ Uncertainty Quantification of Density Reconstruction Using MCMC Method in High-Energy X-ray Radiography ⋮ A Metropolis-Hastings-within-Gibbs sampler for nonlinear hierarchical-Bayesian inverse problems ⋮ Randomized reduced forward models for efficient Metropolis-Hastings MCMC, with application to subsurface fluid flow and capacitance tomography ⋮ Comparison of statistical inversion with iteratively regularized Gauss Newton method for image reconstruction in electrical impedance tomography ⋮ Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems ⋮ Randomize-Then-Optimize for Sampling and Uncertainty Quantification in Electrical Impedance Tomography ⋮ Bayesian Abel Inversion in Quantitative X-Ray Radiography ⋮ Efficient Marginalization-Based MCMC Methods for Hierarchical Bayesian Inverse Problems ⋮ Inverse Problems Involving PDEs with Applications to Imaging ⋮ A statistical reconstruction model for absorption CT with source uncertainty * ⋮ Polynomial Accelerated Solutions to a Large Gaussian Model for Imaging Biofilms: In Theory and Finite Precision ⋮ Dealing with boundary artifacts in MCMC-based deconvolution
Uses Software
This page was built for publication: MCMC-Based Image Reconstruction with Uncertainty Quantification
