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dc.contributor.authorButola, Ankit
dc.contributor.authorKanade, Sheetal Raosaheb
dc.contributor.authorBhatt, Sunil
dc.contributor.authorDubey, Vishesh Kumar
dc.contributor.authorKumar, Anand
dc.contributor.authorAhmad, Azeem
dc.contributor.authorPrasad, Dilip K.
dc.contributor.authorSenthilkumaran, Paramasivam
dc.contributor.authorAhluwalia, Balpreet Singh
dc.contributor.authorMehta, Dalip Singh
dc.date.accessioned2020-11-30T13:48:06Z
dc.date.available2020-11-30T13:48:06Z
dc.date.issued2020-11-16
dc.description.abstractQuantitative phase microscopy (QPM) is a label-free technique that enables monitoring of morphological changes at the subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence properties of the light source and the numerical aperture (NA) of objective lenses. Here, we propose high space-bandwidth quantitative phase imaging using partially spatially coherent digital holographic microscopy (PSC-DHM) assisted with a deep neural network. The PSC source synthesized to improve the spatial sensitivity of the reconstructed phase map from the interferometric images. Further, compatible generative adversarial network (GAN) is used and trained with paired low-resolution (LR) and high-resolution (HR) datasets acquired from the PSC-DHM system. The training of the network is performed on two different types of samples, i.e. mostly homogenous human red blood cells (RBC), and on highly heterogeneous macrophages. The performance is evaluated by predicting the HR images from the datasets captured with a low NA lens and compared with the actual HR phase images. An improvement of 9× in the space-bandwidth product is demonstrated for both RBC and macrophages datasets. We believe that the PSC-DHM + GAN approach would be applicable in single-shot label free tissue imaging, disease classification and other high-resolution tomography applications by utilizing the longitudinal spatial coherence properties of the light source.en_US
dc.identifier.citationButola A, Kanade, Bhatt, Dubey VK, Kumar A, Ahmad A, Prasad DK, Senthilkumaran P, Ahluwalia BS, Mehta DS. High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network. Optics Express. 2020;28(24):36229-36244en_US
dc.identifier.cristinIDFRIDAID 1853617
dc.identifier.doihttps://doi.org/10.1364/OE.402666
dc.identifier.issn1094-4087
dc.identifier.urihttps://hdl.handle.net/10037/19938
dc.language.isoengen_US
dc.publisherOptical Society of Americaen_US
dc.relation.journalOptics Express
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.titleHigh space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural networken_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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