A relaxed Newton-Picard like method for Huber variant of total variation based image restoration

DOI10.1016/j.camwa.2019.02.021zbMath1442.65022OpenAlexW2920635475MaRDI QIDQ2204064

Yanyan Li

Publication date: 2 October 2020

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2019.02.021

zbMATH Keywords

total variationiterative algorithmregularizationGaussian noiseNewton-Picard methodimage restoration

Mathematics Subject Classification ID

Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Uses Software

• UTV
• RecPF

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Nonlinear total variation based noise removal algorithms
• Smooth minimization of non-smooth functions
• Rigorous convergence analysis of alternating variable minimization with multiplier methods for quadratic programming problems with equality constraints
• A unified primal-dual algorithm framework based on Bregman iteration
• On preconditioned and relaxed AVMM methods for quadratic programming problems with equality constraints
• On HSS-based iteration methods for weakly nonlinear systems
• A globally convergent Newton-GMRES method for large sparse systems of nonlinear equations
• Iterative algorithms for total variation-like reconstructions in seismic tomography
• Iterative methods for image deblurring: A Matlab object-oriented approach
• A nonsmooth version of Newton's method
• On Newton-HSS Methods for Systems of Nonliear Equations with Positive-Definite Jacobian Matrices
• Block-Triangular Preconditioners for Systems Arising from Edge-Preserving Image Restoration
• Nonlocal Operators with Applications to Image Processing
• A Fast Total Variation Minimization Method for Image Restoration
• A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
• Iterative Algorithms Based on Decoupling of Deblurring and Denoising for Image Restoration
• Deblurring Images
• Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models
• Semismooth and Semiconvex Functions in Constrained Optimization
• A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
• Rank-Deficient and Discrete Ill-Posed Problems
• Restoring Images Degraded by Spatially Variant Blur
• Local Strong Homogeneity of a Regularized Estimator
• High-Order Total Variation-Based Image Restoration
• Convergence Analysis of Some Algorithms for Solving Nonsmooth Equations
• A Fast Algorithm for Deblurring Models with Neumann Boundary Conditions
• Computational Methods for Inverse Problems
• Structure Tensor Total Variation
• Fast Image Recovery Using Variable Splitting and Constrained Optimization
• Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery
• Total Generalized Variation
• Robust Statistics

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