Preconditioned Douglas-Rachford algorithms for TV- and TGV-regularized variational imaging problems
type: Item
instance of: scholarly article
title: Preconditioned Douglas-Rachford algorithms for TV- and TGV-regularized variational imaging problems
zbMATH Open document ID: 1343.94003
author: K. Bredies
author: Hongpeng Sun
published in: Journal of Mathematical Imaging and Vision
publication date: 2015-11-09T00:00:00Z
Mathematics Subject Classification ID: 94A08
zbMATH DE Number: 6506293
zbMATH Keywords: preconditioned Douglas-Rachford iteration
zbMATH Keywords: primal-dual algorithms
zbMATH Keywords: variational image denoising and deblurring
zbMATH Keywords: total generalized variation
zbMATH Keywords: block preconditioner
Wikidata QID: Q57561961
MaRDI profile type: Publication
full work available at URL: s10851-015-0564-1
OpenAlex ID: W1984845675
cites work: A Douglas--Rachford Type Primal-Dual Method for Solving Inclusions with Mixtures of Composite and Parallel-Sum Type Monotone Operators
cites work: Solving monotone inclusions involving parallel sums of linearly composed maximally monotone operators
cites work: A total variation-based JPEG decompression model
cites work: Preconditioned Douglas--Rachford Splitting Methods for Convex-concave Saddle-point Problems
cites work: Total Generalized Variation
cites work: A Monotone+Skew Splitting Model for Composite Monotone Inclusions in Duality
cites work: A first-order primal-dual algorithm for convex problems with applications to imaging
cites work: On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
cites work: Q4702909
cites work: A dual algorithm for the solution of nonlinear variational problems via finite element approximation
cites work: The Split Bregman Method for L1-Regularized Problems
cites work: Lagrange Multiplier Approach to Variational Problems and Applications
cites work: Splitting Algorithms for the Sum of Two Nonlinear Operators
cites work: An inertial forward-backward algorithm for monotone inclusions
cites work: Monotone Operators and the Proximal Point Algorithm
cites work: Nonlinear total variation based noise removal algorithms
cites work: Q3907169
cites work: Q4256622
cites work: A splitting algorithm for dual monotone inclusions involving cocoercive operators
cites work: Total generalized variation in diffusion tensor imaging
cites work: A unified primal-dual algorithm framework based on Bregman iteration
DBLP publication ID: journals/jmiv/BrediesS15
DOI: 10.1007/S10851-015-0564-1
Recommended article: A preconditioned difference of convex algorithm for truncated quadratic regularization with application to imaging
Recommended article: Dual descent regularization algorithms in variable exponent Lebesgue spaces for imaging
Recommended article: Q4973378
Recommended article: Convergence of variational regularization methods for imaging on Riemannian manifolds
Recommended article: Total variation superiorized conjugate gradient method for image reconstruction
Recommended article: Optimality Conditions for Bilevel Imaging Learning Problems with Total Variation Regularization
Recommended article: Q5465388
Recommended article: Preconditioned Iterative Methods for Algebraic Systems from Multiplicative Half-Quadratic Regularization Image Restorations
Recommended article: On approximation in total variation penalization for image reconstruction and inverse problems
Recommended article: On a nonlinear multigrid algorithm with primal relaxation for the image total variation minimisation
description: scientific article; zbMATH DE number 6506293