\( \chi^2\) test for total variation regularization parameter selection

DOI10.3934/ipi.2020019zbMath1448.65028OpenAlexW3013432954MaRDI QIDQ2188143

Yanyan Li

Publication date: 4 June 2020

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

Full work available at URL: https://doi.org/10.3934/ipi.2020019

zbMATH Keywords

discrepancy principleregressionregularization parametertotal variation regularization\(\chi^2\) test

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) 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) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (3)

Selecting Regularization Parameters for Nuclear Norm--Type Minimization Problems ⋮ A variable projection method for large-scale inverse problems with \(\ell^1\) regularization ⋮ Learning regularization parameters of inverse problems via deep neural networks

Uses Software

• Regularization tools
• na28
• RecPF
• tvreg

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