Rank minimization with applications to image noise removal

DOI10.1016/j.ins.2017.10.047zbMath1436.94008OpenAlexW2766804299MaRDI QIDQ781854

Hui-Yin Yan, Xue Yang, You-Wei Wen, Yu-Mei Huang

Publication date: 20 July 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2017.10.047

zbMATH Keywords

block matchingnuclear normgamma multiplicative noise removalnonlocal self-similarityrank minimization problemwhite Gaussian additive noise removal

Mathematics Subject Classification ID

Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Matrix completion problems (15A83)

Related Items

Multi-band weighted \(l_p\) norm minimization for image denoising, Selecting Regularization Parameters for Nuclear Norm--Type Minimization Problems, A class of modified modulus-based synchronous multisplitting iteration methods for linear complementarity problems, HTR-CTO algorithm for wireless data recovery, Kernel Wiener filtering model with low-rank approximation for image denoising, Patch-based weighted SCAD prior for compressive sensing, Multiplicative Noise Removal: Nonlocal Low-Rank Model and Its Proximal Alternating Reweighted Minimization Algorithm, Tensor \(N\)-tubal rank and its convex relaxation for low-rank tensor recovery, Tensor train rank minimization with nonlocal self-similarity for tensor completion, Two-stage image denoising via an enhanced low-rank prior

Uses Software

• SeDuMi
• SDPT3

Cites Work

• Unnamed Item
• Unnamed Item
• Solving semidefinite-quadratic-linear programs using SDPT3
• Nonlinear total variation based noise removal algorithms
• Fixed point and Bregman iterative methods for matrix rank minimization
• Stein block thresholding for image denoising
• A dual algorithm for the solution of nonlinear variational problems via finite element approximation
• The computational complexity of some problems of linear algebra
• An algorithm for total variation minimization and applications
• Exact matrix completion via convex optimization
• On cone-invariant linear matrix inequalities
• A Convex Variational Model for Restoring Blurred Images with Multiplicative Noise
• A Singular Value Thresholding Algorithm for Matrix Completion
• A Variational Approach to Removing Multiplicative Noise
• On the rank minimization problem over a positive semidefinite linear matrix inequality
• Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
• Compressive Sensing via Nonlocal Low-Rank Regularization
• Robust Image Restoration via Adaptive Low-Rank Approximation and Joint Kernel Regression
• Weighted Schatten <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math> </inline-formula>-Norm Minimization for Image Denoising and Background Subtraction
• Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal
• The Power of Convex Relaxation: Near-Optimal Matrix Completion
• Multiplicative Noise Removal via a Learned Dictionary
• Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach
• New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise
• An Iterative Regularization Method for Total Variation-Based Image Restoration
• A Review of Image Denoising Algorithms, with a New One
• Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients