Algorithms for non-negatively constrained maximum penalized likelihood reconstruction in tomographic imaging

DOI10.3390/A6010136zbMath1461.68244OpenAlexW1987805053MaRDI QIDQ1736549

Xianqiang Yang

Publication date: 26 March 2019

Published in: Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/a6010136

zbMATH Keywords

algorithmsconstrained optimizationpenalized likelihoodtomographic imaging

Mathematics Subject Classification ID

Image analysis in multivariate analysis (62H35) Computing methodologies for image processing (68U10) Biomedical imaging and signal processing (92C55) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

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• Nonlinear total variation based noise removal algorithms
• Monotonicity of quadratic-approximation algorithms
• Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems
• Modified Fisher scoring algorithms using Jacobi or Gauss-Seidel subiterations
• Robust regression: Asymptotics, conjectures and Monte Carlo
• Fisher's Method of Scoring
• A local update strategy for iterative reconstruction from projections
• Recovering Sparse Signals With a Certain Family of Nonconvex Penalties and DC Programming
• Calculation of the fisher information matrix for multidimensional data sets
• A Multiplicative Iterative Algorithm for Box-Constrained Penalized Likelihood Image Restoration

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