Visualizing and improving the robustness of phase retrieval algorithms
Journal Article
·
· Procedia Computer Science
- Argonne National Lab., Argonne, IL (United States). Mathematics and Computer Science Div.
Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup's HIO, behave by introducing a reduced dimensionality problem allowing us to visualize and quantify convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm's ability to converge to the globally optimal solution.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1221604
- Journal Information:
- Procedia Computer Science, Vol. 51, Issue C; ISSN 1877-0509
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Cited by: 4 works
Citation information provided by
Web of Science
Web of Science
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Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging
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journal | August 2017 |
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Towards a quantitative determination of strain in Bragg Coherent X-ray Diffraction Imaging: artefacts and sign convention in reconstructions
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text | January 2019 |
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