A parallel primal-dual splitting method for image restoration
From MaRDI portal
Publication:2279570
DOI10.1016/j.ins.2016.04.004zbMath1427.68341OpenAlexW2332813574WikidataQ62794282 ScholiaQ62794282MaRDI QIDQ2279570
Xuelong Li, Chuan He, Wei Zhang, Chang-Hua Hu
Publication date: 13 December 2019
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2016.04.004
compound \(l_{1}\)-regularizationlarge-scale image restorationlinearized alternating direction method of multipliers (LADMM)parallel primal-dual splitting
Computing methodologies for image processing (68U10) Parallel algorithms in computer science (68W10)
Related Items (1)
Uses Software
Cites Work
- On the ergodic convergence rates of a first-order primal-dual algorithm
- Linearized alternating direction method with adaptive penalty and warm starts for fast solving transform invariant low-rank textures
- A primal-dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms
- Operator splittings, Bregman methods and frame shrinkage in image processing
- Image restoration using total variation with overlapping group sparsity
- A first-order primal-dual algorithm for convex problems with applications to imaging
- A parallel alternating direction method with application to compound \(l_{1}\)-regularized imaging inverse problems
- Constrained Total Variation Deblurring Models and Fast Algorithms Based on Alternating Direction Method of Multipliers
- A New Detail-Preserving Regularization Scheme
- On the $O(1/n)$ Convergence Rate of the Douglas–Rachford Alternating Direction Method
- The Split Bregman Method for L1-Regularized Problems
- A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration
- Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing
- Cartoon-Texture Image Decomposition Using Blockwise Low-Rank Texture Characterization
- A Fast Adaptive Parameter Estimation for Total Variation Image Restoration
- Learning Multiple Linear Mappings for Efficient Single Image Super-Resolution
- Edge Guided Reconstruction for Compressive Imaging
- A primal–dual fixed point algorithm for convex separable minimization with applications to image restoration
- An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems
- Parameter selection for total-variation-based image restoration using discrepancy principle
- A Multiplicative Iterative Algorithm for Box-Constrained Penalized Likelihood Image Restoration
- Nonlocally Centralized Sparse Representation for Image Restoration
- Total Generalized Variation
- Convex analysis and monotone operator theory in Hilbert spaces
This page was built for publication: A parallel primal-dual splitting method for image restoration