Tikhonov-Phillips regularization with operator dependent seminorms

DOI10.1007/s11075-012-9575-9zbMath1245.65049OpenAlexW1974695790MaRDI QIDQ430992

Thomas Kilian Huckle, Matous Sedlacek

Publication date: 26 June 2012

Published in: Numerical Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11075-012-9575-9

zbMATH Keywords

numerical examplesTikhonov-Phillips regularizationGaussian white noiseimage reconstructionsignal reconstructiondiscrete ill-posed problemseminormsdiscrete inverse problems

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (7)

Image deblurring by sparsity constraint on the Fourier coefficients ⋮ Data based regularization for discrete deconvolution problems ⋮ Finite dimensional realization of the FTR method with Raus and Gfrerer type discrepancy principle ⋮ A novel fractional Tikhonov regularization coupled with an improved super-memory gradient method and application to dynamic force identification problems ⋮ Inverse problems for regularization matrices ⋮ Irregularization accelerates iterative regularization ⋮ Discrepancy principles for fractional Tikhonov regularization method leading to optimal convergence rates

Uses Software

• Matlab
• Regularization tools

Cites Work

• Regularization methods for large-scale problems
• Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems
• Data based regularization for discrete deconvolution problems
• A Technique for the Numerical Solution of Certain Integral Equations of the First Kind
• Discrete Inverse Problems
• Smoothing‐Norm Preconditioning for Regularizing Minimum‐Residual Methods
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