Self-optimizing ghost imaging with a genetic algorithm

Liu, B; Shan, X; Zhu, J; Chen, C; Liu, Y; Wang, F; McGloin, D

Self-optimizing ghost imaging with a genetic algorithm

Liu, B

Shan, X Zhu, J Chen, C Liu, Y Wang, F

McGloin, D

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Publisher:: OSA
Publication Type:: Conference Proceeding
Citation:: 2020 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2020 - Proceedings, 2020, 00, pp. 1-2
Issue Date:: 2020-08-01

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Field	Value	Language
dc.contributor.author	Liu, B https://orcid.org/0000-0002-4779-4683
dc.contributor.author	Shan, X
dc.contributor.author	Zhu, J
dc.contributor.author	Chen, C
dc.contributor.author	Liu, Y
dc.contributor.author	Wang, F https://orcid.org/0000-0001-7403-3305
dc.contributor.author	McGloin, D https://orcid.org/0000-0002-0075-4481
dc.date	2020
dc.date.accessioned	2021-04-06T00:20:09Z
dc.date.available	2021-04-06T00:20:09Z
dc.date.issued	2020-08-01
dc.identifier.citation	2020 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2020 - Proceedings, 2020, 00, pp. 1-2
dc.identifier.isbn	9780646825045
dc.identifier.uri	http://hdl.handle.net/10453/147843
dc.description.abstract	© 2019 The Author(s) To simplify the reconstruction algorithms in ghost imaging, we present a feedback-based approach to reduce reconstruction times. We introduce a genetic algorithm to optimize the illumination patterns in real-time to match with the object's shape.
dc.language	en
dc.publisher	OSA
dc.relation	University of Technology Sydney
dc.relation	http://purl.org/au-research/grants/arc/DP190101058
dc.relation	http://purl.org/au-research/grants/arc/DE200100074
dc.relation.ispartof	2020 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2020 - Proceedings
dc.relation.ispartof	Conference on Lasers and Electro-Optics/Pacific Rim
dc.relation.isbasedon	10.1364/CLEOPR.2020.C1G_3
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Self-optimizing ghost imaging with a genetic algorithm
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - IBMD - Initiative for Biomedical Devices
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-06T00:20:08Z
pubs.publication-status	Published
pubs.start-date	2020
pubs.volume	00

Abstract:

© 2019 The Author(s) To simplify the reconstruction algorithms in ghost imaging, we present a feedback-based approach to reduce reconstruction times. We introduce a genetic algorithm to optimize the illumination patterns in real-time to match with the object's shape.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/147843