Visualizing and improving the robustness of phase retrieval algorithms

Tripathi, Ashish; Leyffer, Sven; Munson, Todd; Wild, Stefan M.

doi:10.1016/j.procs.2015.05.205

Title: Visualizing and improving the robustness of phase retrieval algorithms

Journal Article · Mon Jun 01 00:00:00 EDT 2015 · Procedia Computer Science

DOI:https://doi.org/10.1016/j.procs.2015.05.205· OSTI ID:1221604

Tripathi, Ashish ^[1]; Leyffer, Sven ^[1]; Munson, Todd ^[1]; Wild, Stefan M. ^[1]

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.

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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

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Cited by: 4 works

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References (7)

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Ptychographic X-ray computed tomography at the nanoscale Dierolf, Martin; Menzel, Andreas; Thibault, Pierre Nature, Vol. 467, Issue 7314 https://doi.org/10.1038/nature09419	journal	September 2010
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Phase retrieval and saddle-point optimization Marchesini, Stefano Journal of the Optical Society of America A, Vol. 24, Issue 10 https://doi.org/10.1364/JOSAA.24.003289	journal	January 2007
Unconstrained Optimization of Real Functions in Complex Variables Sorber, Laurent; Barel, Marc Van; Lathauwer, Lieven De SIAM Journal on Optimization, Vol. 22, Issue 3 https://doi.org/10.1137/110832124	journal	January 2012
Dichroic coherent diffractive imaging Tripathi, A.; Mohanty, J.; Dietze, S. H. Proceedings of the National Academy of Sciences, Vol. 108, Issue 33 https://doi.org/10.1073/pnas.1104304108	journal	August 2011

Cited By (4)

Towards a quantitative determination of strain in Bragg Coherent X-ray Diffraction Imaging: artefacts and sign convention in reconstructions Carnis, Jérôme; Gao, Lu; Labat, Stéphane Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-019-53774-2	journal	November 2019
Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging Ulvestad, A.; Nashed, Y.; Beutier, G. Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/s41598-017-09582-7	journal	August 2017
Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation Ulvestad, A.; Menickelly, M.; Wild, S. M. AIP Advances, Vol. 8, Issue 1 https://doi.org/10.1063/1.5017596	journal	January 2018
Towards a quantitative determination of strain in Bragg Coherent X-ray Diffraction Imaging: artefacts and sign convention in reconstructions Carnis, Jerome; Gao, Lu; Labat, Stéphane Deutsches Elektronen-Synchrotron, DESY, Hamburg https://doi.org/10.3204/pubdb-2020-00283	text	January 2019

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97 MATHEMATICS AND COMPUTING
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phase retrieval algorithms
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