JOURNAL ARTICLE

Automatic regularization for tomographic image reconstruction

Eduardo X. MiquelesPatricio Guerrero

Year: 2020 Journal:   Results in Applied Mathematics Vol: 6 Pages: 100088-100088   Publisher: Elsevier BV

Abstract

The phase retrieval process of imaging a sample can be modeled as a simple convolution process. Sometimes, such a convolution depends on physical parameters of the sample which are difficult to estimate a priori. In this case, a blind choice for those parameters usually lead to wrong results, e.g., extracting information from the reconstructed images. In this manuscript, we propose a simple connection between phase-retrieval algorithms and optimization strategies, which lead us to ways of numerically determining the physical parameters. Keywords: Regularization, Phase, Tomography, Synchrotron

Keywords:
Regularization (linguistics) Convolution (computer science) A priori and a posteriori Computer science Artificial intelligence Inverse problem Tomographic reconstruction Phase retrieval Deconvolution Algorithm Simple (philosophy) Sample (material) Iterative reconstruction Pattern recognition (psychology) Process (computing) Computer vision Mathematics Fourier transform Physics Mathematical analysis

Metrics

2
Cited By
0.25
FWCI (Field Weighted Citation Impact)
27
Refs
0.54
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Advanced X-ray Imaging Techniques
Physical Sciences →  Physics and Astronomy →  Radiation
Advanced X-ray and CT Imaging
Physical Sciences →  Engineering →  Biomedical Engineering
Electron and X-Ray Spectroscopy Techniques
Physical Sciences →  Materials Science →  Surfaces, Coatings and Films
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