Parameter selection for HOTV regularization

DOI10.1016/j.apnum.2017.10.010zbMath1379.65032arXiv1608.04819OpenAlexW2963130125MaRDI QIDQ1686204

Toby Sanders

Publication date: 21 December 2017

Published in: Applied Numerical Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1608.04819

zbMATH Keywords

regularizationinverse problemsimage reconstructionparameter selectionnumerical resulthigher-order total variation

Mathematics Subject Classification ID

Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Numerical solutions to equations with linear operators (65J10) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20) Linear operators and ill-posed problems, regularization (47A52) Numerical solution to inverse problems in abstract spaces (65J22)

Uses Software

• Matlab
• TVAL3

Cites Work

• Unnamed Item
• Unnamed Item
• Nonlinear total variation based noise removal algorithms
• An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers
• Tikhonov regularization and the L-curve for large discrete ill-posed problems
• A combined first and second order variational approach for image reconstruction
• Composite SAR imaging using sequential joint sparsity
• Structure-texture image decomposition -- modeling, algorithms, and parameter selection
• An efficient augmented Lagrangian method with applications to total variation minimization
• Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time
• Improved Total Variation-Type Regularization Using Higher Order Edge Detectors
• Edge-preserving and scale-dependent properties of total variation regularization
• High-Order Total Variation-Based Image Restoration
• Sparsity and incoherence in compressive sampling
• Parameter selection for total-variation-based image restoration using discrepancy principle
• Higher Degree Total Variation (HDTV) Regularization for Image Recovery
• Scale Recognition, Regularization Parameter Selection, and Meyer's G Norm in Total Variation Regularization
• Polynomial Fitting for Edge Detection in Irregularly Sampled Signals and Images
• Total Generalized Variation
• Sparse Image and Signal Processing

This page was built for publication: Parameter selection for HOTV regularization