Automatic regularization for tomographic image reconstruction
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.,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-05-01
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| Series: | Results in Applied Mathematics |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590037419300883 |
| Summary: | 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 |
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| ISSN: | 2590-0374 |