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Machine learning methods for digital holography and diffractive optics

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dc.contributor.authorCheremkhin, P.
dc.contributor.authorEvtikhiev, N.
dc.contributor.authorKrasnov, V.
dc.contributor.authorRodin, V.
dc.contributor.authorRymov, D.
dc.contributor.authorStarikov, R.
dc.contributor.authorЧерёмхин, Павел Аркадьевич
dc.contributor.authorЕвтихиев, Николай Николаевич
dc.contributor.authorРодин, Владислав Геннадьевич
dc.contributor.authorРымов, Дмитрий Андреевич
dc.contributor.authorСтариков, Ростислав Сергеевич
dc.date.accessioned2024-11-26T13:05:57Z
dc.date.available2024-11-26T13:05:57Z
dc.date.issued2020
dc.description.abstract© 2020 The Authors. Published by Elsevier B.V.With active advancements in computer and computational technologies, deep learning has found its way into many fields. Recently it has become an active topic of research in diffractive optics and holography. Deep leaning techniques have been shown to benefit greatly from abundant information offered by using both amplitude and phase of the optical field. These techniques can be applied for image reconstruction, zero-order suppression, hologram generation, etc. In this paper various learning based methods for enhancing digital and computer-generated holography are analysed. We demonstrate a deep learning model for generating diffractive optical elements from an arbitrary intensity-only image. Numerical evaluation of model's performance has shown that generated diffractive optical elements are of acceptable quality.
dc.format.extentС. 440-444
dc.identifier.citationMachine learning methods for digital holography and diffractive optics / Cheremkhin, P. [et al.] // Procedia Computer Science. - 2020. - 169. - P. 440-444. - 10.1016/j.procs.2020.02.243
dc.identifier.doi10.1016/j.procs.2020.02.243
dc.identifier.urihttps://www.doi.org/10.1016/j.procs.2020.02.243
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85084495388&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/21705
dc.relation.ispartofProcedia Computer Science
dc.titleMachine learning methods for digital holography and diffractive optics
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.volume169
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