An inner–outer iterative method for edge preservation in image restoration and reconstruction *

DOI10.1088/1361-6420/abb299zbMath1454.65012arXiv1912.13103OpenAlexW3081276093MaRDI QIDQ5139316

Silvia Gazzola, James G. Nagy, Eric L. Miller, Oğuz Semerci, Misha E. Kilmer

Publication date: 8 December 2020

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.13103

zbMATH Keywords

tomographyKrylov subspace methodsimage deblurringparameter choice strategiesedge enhancementiterative reweightinghybrid regularization methods

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22) Computing methodologies for image processing (68U10) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Iterative numerical methods for linear systems (65F10)

Related Items (6)

Edge adaptive hybrid regularization model for image deblurring ⋮ A computational framework for edge-preserving regularization in dynamic inverse problems ⋮ Learning regularization parameters of inverse problems via deep neural networks ⋮ Iterative hybrid regularization for extremely noisy full models in single particle analysis ⋮ Applying smoothing technique and semi-proximal ADMM for image deblurring ⋮ Foreword to special issue of Inverse Problems on modern challenges in imaging

Uses Software

• LSQR
• Regularization tools
• AIR tools
• LSMR
• IR Tools
• CRAIG

Cites Work

• Nonlinear total variation based noise removal algorithms
• A new zero-finder for Tikhonov regularization
• A bidiagonalization algorithm for solving large and sparse ill-posed systems of linear equations
• An implicit shift bidiagonalization algorithm for ill-posed systems
• Flexible GMRES for total variation regularization
• On Krylov projection methods and Tikhonov regularization
• AIR tools II: algebraic iterative reconstruction methods, improved implementation
• IR tools: a MATLAB package of iterative regularization methods and large-scale test problems
• Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
• Iterated preconditioned LSQR method for inverse problems on unstructured grids
• Generalized Arnoldi--Tikhonov Method for Sparse Reconstruction
• Hybrid and Iteratively Reweighted Regularization by Unbiased Predictive Risk and Weighted GCV for Projected Systems
• LSMR: An Iterative Algorithm for Sparse Least-Squares Problems
• Iterative Parameter-Choice and Multigrid Methods for Anisotropic Diffusion Denoising
• Deblurring Images
• A Bidiagonalization-Regularization Procedure for Large Scale Discretizations of Ill-Posed Problems
• LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
• Rank-Deficient and Discrete Ill-Posed Problems
• Computational Methods for Inverse Problems
• Iterative Methods for Total Variation Denoising
• Flexible Krylov Methods for $\ell_p$ Regularization
• A Framework for Regularization via Operator Approximation
• A Projection‐Based Approach to General‐Form Tikhonov Regularization
• An Iterative Regularization Method for Total Variation-Based Image Restoration
• Image Processing and Analysis
• Laplace-distributed increments, the Laplace prior, and edge-preserving regularization
• Unnamed Item

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