A parallel splitting augmented Lagrangian method for two-block separable convex programming with application in image processing

DOI10.1155/2020/6872810zbMATH Open1544.90148MaRDI QIDQ6534843

Yongrui Duan, Jing Liu, Tonghui Wang

Publication date: 18 May 2021

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Mathematics Subject Classification ID

Convex programming (90C25) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

• Linearized alternating direction method with adaptive penalty and warm starts for fast solving transform invariant low-rank textures
• An alternating direction method with increasing penalty for stable principal component pursuit
• An efficient algorithm for batch images alignment with adaptive rank-correction term
• TILT: transform invariant low-rank textures
• Traditional and recent approaches in background modeling for foreground detection: an overview
• Multiplier and gradient methods
• Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
• Robust principal component analysis?
• A Singular Value Thresholding Algorithm for Matrix Completion
• Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
• On Full Jacobian Decomposition of the Augmented Lagrangian Method for Separable Convex Programming
• From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
• Atomic Decomposition by Basis Pursuit
• A splitting method for separable convex programming
• A partially parallel splitting method for multiple-block separable convex programming with applications to robust PCA

This page was built for publication: A parallel splitting augmented Lagrangian method for two-block separable convex programming with application in image processing

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6534843)