A new linearized split Bregman iterative algorithm for image reconstruction in sparse-view X-ray computed tomography
type: Item
instance of: scholarly article
title: A new linearized split Bregman iterative algorithm for image reconstruction in sparse-view X-ray computed tomography
zbMATH Open document ID: 1443.92107
author: C. Chen
author: Guo-Liang Xu
publication date: 2020-10-12T00:00:00Z
Mathematics Subject Classification ID: 92C55
zbMATH DE Number: 7259727
zbMATH Keywords: linearized split Bregman iteration
zbMATH Keywords: compressed sensing
zbMATH Keywords: robust image reconstruction
zbMATH Keywords: sparse views
zbMATH Keywords: X-ray computed tomography
describes a project that uses: FTVd
MaRDI profile type: Publication
full work available at URL: j.camwa.2016.01.003
OpenAlex ID: W2307975029
cites work: Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?
cites work: Gradient-flow-based semi-implicit finite-element method and its convergence analysis for image reconstruction
cites work: Compressed sensing
cites work: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
cites work: Stable signal recovery from incomplete and inaccurate measurements
cites work: The restricted isometry property and its implications for compressed sensing
cites work: Nonlinear total variation based noise removal algorithms
cites work: Inversion of electron tomography images using \(L^2\)-gradient flows -- computational methods
cites work: Level set methods and dynamic implicit surfaces
cites work: Iterative Methods for Total Variation Denoising
cites work: The Split Bregman Method for L1-Regularized Problems
cites work: Computational inversion of electron micrographs using \(L^{2}\)-gradient flows --- convergence analysis
cites work: Blended finite element method and its convergence for three-dimensional image reconstruction using \(L^2\)-gradient flow
cites work: Geometric applications of the split Bregman method: segmentation and surface reconstruction
cites work: Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
cites work: Lectures on numerical methods for non-linear variational problems
cites work: Q3995612
cites work: Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
cites work: Split Bregman Methods and Frame Based Image Restoration
cites work: Minimizers of Cost-Functions Involving Nonsmooth Data-Fidelity Terms. Application to the Processing of Outliers
cites work: Aspects of Total Variation RegularizedL1Function Approximation
cites work: Efficient Minimization Methods of Mixed l2-l1 and l1-l1 Norms for Image Restoration
cites work: Augmented Lagrangian method for total variation restoration with non-quadratic fidelity
cites work: Q4235027
cites work: Q5580053
cites work: An Iterative Regularization Method for Total Variation-Based Image Restoration
cites work: An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise
cites work: Fixed point and Bregman iterative methods for matrix rank minimization
cites work: Linearized Alternating Direction Method of Multipliers for Constrained Linear Least-Squares Problem
cites work: Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
DOI: 10.1016/J.CAMWA.2016.01.003
published in: Computers & Mathematics with Applications
description: scientific article; zbMATH DE number 7259727