Uniform Penalty inversion of two-dimensional NMR relaxation data

DOI10.1088/1361-6420/33/1/015003zbMath1361.65098arXiv1609.00324OpenAlexW3098847158MaRDI QIDQ2971440

Villiam Bortolotti, Germana Landi, P. Fantazzini, R. J. S. Brown, Fabiana Zama

Publication date: 5 April 2017

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

Full work available at URL: https://arxiv.org/abs/1609.00324

zbMATH Keywords

inverse Laplace transformnumerical experimentill-posed problemnuclear magnetic resonancedata inversionspatially adapted regularization parameternonnegative Tikhonov regularizationuniform penalty algorithm

Mathematics Subject Classification ID

Numerical methods for integral equations (65R20) Biomedical imaging and signal processing (92C55) Laplace transform (44A10) Numerical methods for integral transforms (65R10) Numerical methods for ill-posed problems for integral equations (65R30) Fredholm integral equations (45B05) Linear integral equations (45A05)

Related Items (6)

Neural blind deconvolution with Poisson data ⋮ IR tools: a MATLAB package of iterative regularization methods and large-scale test problems ⋮ Iterative algorithms for a non-linear inverse problem in atmospheric lidar ⋮ 2DNMR data inversion using locally adapted multi-penalty regularization ⋮ A variational approach to Gibbs artifacts removal in MRI ⋮ Constrained and unconstrained deep image prior optimization models with automatic regularization

Uses Software

• UpenWin

Cites Work

• UpenWin
• Automated regularization parameter selection in multi-scale total variation models for image restoration
• Review of inverse Laplace transform algorithms for Laplace-space numerical approaches
• Expected absolute value estimators for a spatially adapted regularization parameter choice rule in L ¹ -TV-based image restoration
• Computational Methods for Inverse Problems
• Solving Fredholm integrals of the first kind with tensor product structure in 2 and 2.5 dimensions
• On the filtering effect of iterative regularization algorithms for discrete inverse problems
• Least Squares Methods for Ill-Posed Problems with a Prescribed Bound

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