Regularization parameter selection and an efficient algorithm for total variation-regularized positron emission tomography

DOI10.1007/s11075-010-9427-4zbMath1217.65117OpenAlexW1974364974MaRDI QIDQ542445

John Goldes, Johnathan M. Bardsley

Publication date: 10 June 2011

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

Full work available at URL: https://doi.org/10.1007/s11075-010-9427-4

zbMATH Keywords

convergencenumerical examplesinverse problemspositron emission tomographystatistical methodstotal variation regularizationPoisson likelihood fit-to-data functionregularization parameter selection

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Existence theories for optimal control problems involving ordinary differential equations (49J15) Inverse problems in optimal control (49N45) Existence of optimal solutions to problems involving randomness (49J55)

Related Items (2)

Discrete tomography with unknown intensity levels using higher-order statistics ⋮ An automatic regularization parameter selection algorithm in the total variation model for image deblurring

Uses Software

• KELLEY

Cites Work

• An efficient computational method for total variation-penalized Poisson likelihood estimation
• Statistical and computational inverse problems.
• Total variation-penalized Poisson likelihood estimation for ill-posed problems
• An Iterative Method for Edge-Preserving MAP Estimation When Data-Noise Is Poisson
• Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation
• Hierarchical regularization for edge-preserving reconstruction of PET images
• A Nonnegatively Constrained Convex Programming Method for Image Reconstruction
• Fast, robust total variation-based reconstruction of noisy, blurred images
• Unnamed Item

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