UPRE method for total variation parameter selection

DOI10.1016/j.sigpro.2010.02.025zbMath1194.94109OpenAlexW2078186482MaRDI QIDQ985621

Hongbin Guo, Brendt Wohlberg, Youzuo Lin

Publication date: 6 August 2010

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://www.osti.gov/biblio/957756

zbMATH Keywords

inverse problemparameter selectiontotal variation regularizationlarge scale problem

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (8)

\( \chi^2\) test for total variation regularization parameter selection ⋮ Optimization methods for regularization-based ill-posed problems: a survey and a multi-objective framework ⋮ Learning regularization parameters of inverse problems via deep neural networks ⋮ Multisplitting for regularized least squares with Krylov subspace recycling ⋮ Unbiased predictive risk estimation of the Tikhonov regularization parameter: convergence with increasing rank approximations of the singular value decomposition ⋮ Automated parameter selection for total variation minimization in image restoration ⋮ Constrained and unconstrained deep image prior optimization models with automatic regularization ⋮ Iterative choice of the optimal regularization parameter in TV image restoration

Uses Software

• BRENT

Cites Work

• Nonlinear total variation based noise removal algorithms
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• A Stochastic Estimator of the Trace of the Influence Matrix for Laplacian Smoothing Splines
• A Newton root-finding algorithm for estimating the regularization parameter for solving ill-conditioned least squares problems
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• The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems
• Rank-Deficient and Discrete Ill-Posed Problems
• Computational Methods for Inverse Problems
• Calculation of Gauss Quadrature Rules
• Some Comments on C P
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