The “Minimum Reconstruction Error” Choice of Regularization Parameters: Some More Efficient Methods and Their Application to Deconvolution Problems

DOI10.1137/0916080zbMath0840.65138OpenAlexW2083236768MaRDI QIDQ4859541

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Publication date: 30 June 1996

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/9be7a49e3492ab5620becdc79e41d0d5c7d4ebac

zbMATH Keywords

regularizationinverse problemsill-posed problemsdeconvolutionsmoothing parametersgeneralized cross validation methodautomatic stabilizationill-conditioned least squares problems

Mathematics Subject Classification ID

Numerical smoothing, curve fitting (65D10) Nonparametric estimation (62G05) Numerical methods for integral equations (65R20) Numerical methods for ill-posed problems for integral equations (65R30) Integral equations of the convolution type (Abel, Picard, Toeplitz and Wiener-Hopf type) (45E10)

Related Items (5)

Operator-theoretic and regularization approaches to ill-posed problems ⋮ Local behavior of sparse analysis regularization: applications to risk estimation ⋮ Comparing different types of approximators for choosing the parameters in the regularization of ill-posed problems ⋮ Automated data-driven selection of the hyperparameters for total-variation-based texture segmentation ⋮ Ill-Posed Problems: Operator Methodologies of Resolution and Regularization

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