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HoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network

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dc.contributor.authorSvistunov, A. S.
dc.contributor.authorRymov, D. A.
dc.contributor.authorStarikov, R. S.
dc.contributor.authorCheremkhin, P. A.
dc.contributor.authorСвистунов, Андрей Сергеевич
dc.contributor.authorРымов, Дмитрий Андреевич
dc.contributor.authorСтариков, Ростислав Сергеевич
dc.contributor.authorЧерёмхин, Павел Аркадьевич
dc.date.accessioned2024-12-28T11:15:14Z
dc.date.available2024-12-28T11:15:14Z
dc.date.issued2023
dc.description.abstractReconstruction of 3D scenes from digital holograms is an important task in different areas of science, such as biology, medicine, ecology, etc. A lot of parameters, such as the object’s shape, number, position, rate and density, can be extracted. However, reconstruction of off-axis and especially inline holograms can be challenging due to the presence of optical noise, zero-order image and twin image. We have used a deep-multibranch neural network model, which we call HoloForkNet, to reconstruct different 2D sections of a 3D scene from a single inline hologram. This paper describes the proposed method and analyzes its performance for different types of objects. Both computer-generated and optically registered digital holograms with resolutions up to 2048 × 2048 pixels were reconstructed. High-quality image reconstruction for scenes consisting of up to eight planes was achieved. The average structural similarity index (SSIM) for 3D test scenes with eight object planes was 0.94. The HoloForkNet can be used to reconstruct 3D scenes consisting of micro- and macro-objects.
dc.identifier.citationHoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network / Svistunov, A. S. [et al.] // Applied Sciences (Switzerland). - 2023. - 13. - № 10. - 10.3390/app13106125
dc.identifier.doi10.3390/app13106125
dc.identifier.urihttps://www.doi.org/10.3390/app13106125
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85160633533&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000994401700001
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/30202
dc.relation.ispartofApplied Sciences (Switzerland)
dc.subjectThree-Dimensional Imaging
dc.subjectDigital holography
dc.subjectDigital Imaging
dc.subjectDigital Holographic Microscopy
dc.subjectHigh-Content Screening
dc.subjectImage Processing
dc.subjectSimilarity (geometry)
dc.subject3D reconstruction
dc.titleHoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network
dc.typeArticle
dspace.entity.typePublication
oaire.citation.issue10
oaire.citation.volume13
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