Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography

Cited 49 time in webofscience Cited 37 time in scopus
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dc.contributor.authorChoi, Gunhoko
dc.contributor.authorRyu, DongHunko
dc.contributor.authorJo, YoungJuko
dc.contributor.authorKim, Young Seoko
dc.contributor.authorPark, Weisunko
dc.contributor.authorMin, Hyun-seokko
dc.contributor.authorPark, YongKeunko
dc.date.accessioned2019-03-19T01:51:11Z-
dc.date.available2019-03-19T01:51:11Z-
dc.date.created2019-03-11-
dc.date.created2019-03-11-
dc.date.created2019-03-11-
dc.date.created2019-03-11-
dc.date.created2019-03-11-
dc.date.issued2019-02-
dc.identifier.citationOPTICS EXPRESS, v.27, no.4, pp.4927 - 4943-
dc.identifier.issn1094-4087-
dc.identifier.urihttp://hdl.handle.net/10203/251800-
dc.description.abstractWe present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concluded by applying our technique to reduce the temporally changing noise emerging from focal drift in time-lapse imaging of biological cells. This reduction cannot be performed using other optical methods for denoising. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement-
dc.languageEnglish-
dc.publisherOPTICAL SOC AMER-
dc.titleCycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography-
dc.typeArticle-
dc.identifier.wosid000459152800108-
dc.identifier.scopusid2-s2.0-85061963210-
dc.type.rimsART-
dc.citation.volume27-
dc.citation.issue4-
dc.citation.beginningpage4927-
dc.citation.endingpage4943-
dc.citation.publicationnameOPTICS EXPRESS-
dc.identifier.doi10.1364/OE.27.004927-
dc.contributor.localauthorPark, YongKeun-
dc.contributor.nonIdAuthorChoi, Gunho-
dc.contributor.nonIdAuthorRyu, DongHun-
dc.contributor.nonIdAuthorJo, YoungJu-
dc.contributor.nonIdAuthorKim, Young Seo-
dc.contributor.nonIdAuthorPark, Weisun-
dc.contributor.nonIdAuthorMin, Hyun-seok-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordPlusRED-BLOOD-CELLS-
dc.subject.keywordPlusIMAGE-RECONSTRUCTION-
dc.subject.keywordPlusPHASE-
dc.subject.keywordPlusFIELD-
dc.subject.keywordPlusMICROSCOPY-
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