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A theoretical framework for the regularization of Poisson likelihood estimation problems

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Publication:968756
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DOI10.3934/ipi.2010.4.11zbMath1189.65105OpenAlexW2086468497MaRDI QIDQ968756

Johnathan M. Bardsley

Publication date: 6 May 2010

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

Full work available at URL: https://doi.org/10.3934/ipi.2010.4.11


zbMATH Keywords

regularizationvariational problemslinear operator equationill-posedPoisson likelihoodmathematical imaging


Mathematics Subject Classification ID

Numerical solutions to equations with linear operators (65J10) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20) Linear operators and ill-posed problems, regularization (47A52)


Related Items (4)

Adaptative regularization parameter for Poisson noise with a bilevel approach: application to spectral computerized tomography ⋮ Morozov principle for Kullback-Leibler residual term and Poisson noise ⋮ Iteratively regularized Newton-type methods for general data misfit functionals and applications to Poisson data ⋮ Techniques for regularization parameter and hyper-parameter selection in PET and SPECT imaging




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