Designing Optimal Spectral Filters for Inverse Problems

DOI10.1137/100812938zbMath1269.65040OpenAlexW2084409811MaRDI QIDQ3116478

Julianne Chung, Dianne P. O'Leary, Matthias Chung

Publication date: 23 February 2012

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

Full work available at URL: https://semanticscholar.org/paper/be841779cb1e6d800655b28ffb4afc004747f71e

zbMATH Keywords

stochastic programmingregularizationnumerical examplessingular value decompositionoptimal filteringoptimal designmachine learningWiener filterimage deblurringspectral filteringempirical riskill-posed inverse problemsBayes risk minimizationoptimal spectral filterssignal and image deconvolution

Mathematics Subject Classification ID

Related Items (15)

Learning nonlocal regularization operators ⋮ Optimal Low-rank Approximations of Bayesian Linear Inverse Problems ⋮ Learning regularization parameters for general-form Tikhonov ⋮ Learning spectral windowing parameters for regularization using unbiased predictive risk and generalized cross validation techniques for multiple data sets ⋮ Optimal filters from calibration data for image deconvolution with data acquisition error ⋮ Bilevel inverse problems in neuromorphic imaging ⋮ Invertible residual networks in the context of regularization theory for linear inverse problems ⋮ Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems ⋮ Learning regularization parameters of inverse problems via deep neural networks ⋮ A bilevel approach for parameter learning in inverse problems ⋮ Optimal regularized low rank inverse approximation ⋮ A tutorial on rank-based coefficient estimation for censored data in small- and large-scale problems ⋮ Modern regularization methods for inverse problems ⋮ Hybrid projection methods for large-scale inverse problems with mixed Gaussian priors ⋮ Optimal Regularized Inverse Matrices for Inverse Problems

Uses Software

• rMPC
• SUTIL

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