Multiplicative Noise Removal: Nonlocal Low-Rank Model and Its Proximal Alternating Reweighted Minimization Algorithm

DOI10.1137/20M1313167zbMath1501.94003arXiv2002.07633OpenAlexW3085462114MaRDI QIDQ5143309

Xiaoxia Liu, Chen Xu, Jian Lu, Yuesheng Xu, Li-Xin Shen

Publication date: 11 January 2021

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.07633

zbMATH Keywords

image restorationmultiplicative noise removalnonlocal low-rank regularization

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22) Nonconvex programming, global optimization (90C26) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Applications of functional analysis in optimization, convex analysis, mathematical programming, economics (46N10) Vector spaces, linear dependence, rank, lineability (15A03)

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Cites Work

• Unnamed Item
• Reduced rank regression via adaptive nuclear norm penalization
• Proximal alternating linearized minimization for nonconvex and nonsmooth problems
• Image recovery via nonlocal operators
• Multiplicative noise removal in imaging: an exp-model and its fixed-point proximity algorithm
• Rank minimization with applications to image noise removal
• Removing multiplicative noise by Douglas-Rachford splitting methods
• Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
• Nonsmooth analysis of singular values. I: Theory
• Nonsmooth analysis of singular values. II: Applications
• Two-Level Convex Relaxed Variational Model for Multiplicative Denoising
• A Convex Variational Model for Restoring Blurred Images with Multiplicative Noise
• A Two-Stage Image Segmentation Method for Blurry Images with Poisson or Multiplicative Gamma Noise
• Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
• A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model
• A New Total Variation Method for Multiplicative Noise Removal
• A Variational Approach to Removing Multiplicative Noise
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Speckle Reduction via Higher Order Total Variation Approach
• Compressive Sensing via Nonlocal Low-Rank Regularization
• Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation
• Convergence of Proximal Iteratively Reweighted Nuclear Norm Algorithm for Image Processing
• Restoration of Poissonian Images Using Alternating Direction Optimization
• A New Multiplicative Denoising Variational Model Based on $m$th Root Transformation
• Multiplicative Noise Removal via a Learned Dictionary
• A Review of Image Denoising Algorithms, with a New One