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dc.contributor.authorBarbastathis, George
dc.date.accessioned2021-02-09T19:32:02Z
dc.date.available2021-02-09T19:32:02Z
dc.date.issued2020-02
dc.identifier.isbn9781510632615
dc.identifier.isbn9781510632622
dc.identifier.issn1996-756X
dc.identifier.urihttps://hdl.handle.net/1721.1/129723
dc.description.abstractIt has recently been recognized that compressed sensing, especially dictionaries and related methods, formally map to machine learning architectures, e.g. recurrent neural networks. This has led to rapid growth in algorithms and methods based on deep neural networks (but not only) for solving a variety of inverse and computational imaging problems. In this paper, we review these developments in the specific context of quantitative phase imaging and emphasizing the impact of object power spectral density and noise properties on the quality of the reconstructions.en_US
dc.language.isoen
dc.publisherSPIEen_US
dc.relation.isversionof10.1117/12.2554397en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSPIEen_US
dc.titleOn the use of machine learning for solving computational imaging problemsen_US
dc.typeArticleen_US
dc.identifier.citationBarbastathis, George. "On the use of machine learning for solving computational imaging problems." Proceedings of SPIE (February 2020) © 2020 SPIE.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.departmentSingapore-MIT Alliance in Research and Technology (SMART)en_US
dc.relation.journalProceedings of SPIEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-06-22T19:01:01Z
dspace.date.submission2020-06-22T19:01:03Z
mit.journal.volume11249en_US
mit.licensePUBLISHER_POLICY


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