Machine learning holography for 3D particle field imaging

We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate mea...

Full description

Saved in:
Bibliographic Details
Published in:Optics express 2020-02, Vol.28 (3), p.2987-2999
Main Authors: Shao, Siyao, Mallery, Kevin, Kumar, S Santosh, Hong, Jiarong
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c426t-3a9db5d03d7c41b2db91a3cb1fecec52c4029b35a0997a12115914b1e2a5a283
cites cdi_FETCH-LOGICAL-c426t-3a9db5d03d7c41b2db91a3cb1fecec52c4029b35a0997a12115914b1e2a5a283
container_end_page 2999
container_issue 3
container_start_page 2987
container_title Optics express
container_volume 28
creator Shao, Siyao
Mallery, Kevin
Kumar, S Santosh
Hong, Jiarong
description We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate measurement of individual particles is crucial. Assessments on both synthetic and experimental holograms demonstrate a significant improvement in particle extraction rate, localization accuracy and speed compared to prior methods over a wide range of particle concentrations, including highly dense concentrations where other methods are unsuitable. Our approach can be potentially extended to other types of computational imaging tasks with similar features.
doi_str_mv 10.1364/OE.379480
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2370532850</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2370532850</sourcerecordid><originalsourceid>FETCH-LOGICAL-c426t-3a9db5d03d7c41b2db91a3cb1fecec52c4029b35a0997a12115914b1e2a5a283</originalsourceid><addsrcrecordid>eNpNkDtPwzAYRS0EoqUw8AeQRxhS_KzjEZXwkIq6dLds50sb5CbBTof-e4JSENO9w9HV1UHolpI55QvxuC7mXGmRkzM0pUSLTJBcnf_rE3SV0ichVCitLtGEM8qoVnKK9If1u7oBHMDGpm62eNeGdhtttzviqo2YP-POxr72AXBVQyhxvbfbAbxGF5UNCW5OOUObl2KzfMtW69f35dMq84It-oxbXTpZEl4qL6hjpdPUcu9oBR68ZF4Qph2Xlmit7HCLSk2Fo8CstCznM3Q_znax_TpA6s2-Th5CsA20h2QYV0RylksyoA8j6mObUoTKdHE4G4-GEvMjyqwLM4oa2LvT7MHtofwjf83wb_RnYXA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2370532850</pqid></control><display><type>article</type><title>Machine learning holography for 3D particle field imaging</title><source>EZB Electronic Journals Library</source><creator>Shao, Siyao ; Mallery, Kevin ; Kumar, S Santosh ; Hong, Jiarong</creator><creatorcontrib>Shao, Siyao ; Mallery, Kevin ; Kumar, S Santosh ; Hong, Jiarong</creatorcontrib><description>We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate measurement of individual particles is crucial. Assessments on both synthetic and experimental holograms demonstrate a significant improvement in particle extraction rate, localization accuracy and speed compared to prior methods over a wide range of particle concentrations, including highly dense concentrations where other methods are unsuitable. Our approach can be potentially extended to other types of computational imaging tasks with similar features.</description><identifier>ISSN: 1094-4087</identifier><identifier>EISSN: 1094-4087</identifier><identifier>DOI: 10.1364/OE.379480</identifier><identifier>PMID: 32121975</identifier><language>eng</language><publisher>United States</publisher><ispartof>Optics express, 2020-02, Vol.28 (3), p.2987-2999</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c426t-3a9db5d03d7c41b2db91a3cb1fecec52c4029b35a0997a12115914b1e2a5a283</citedby><cites>FETCH-LOGICAL-c426t-3a9db5d03d7c41b2db91a3cb1fecec52c4029b35a0997a12115914b1e2a5a283</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32121975$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shao, Siyao</creatorcontrib><creatorcontrib>Mallery, Kevin</creatorcontrib><creatorcontrib>Kumar, S Santosh</creatorcontrib><creatorcontrib>Hong, Jiarong</creatorcontrib><title>Machine learning holography for 3D particle field imaging</title><title>Optics express</title><addtitle>Opt Express</addtitle><description>We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate measurement of individual particles is crucial. Assessments on both synthetic and experimental holograms demonstrate a significant improvement in particle extraction rate, localization accuracy and speed compared to prior methods over a wide range of particle concentrations, including highly dense concentrations where other methods are unsuitable. Our approach can be potentially extended to other types of computational imaging tasks with similar features.</description><issn>1094-4087</issn><issn>1094-4087</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNpNkDtPwzAYRS0EoqUw8AeQRxhS_KzjEZXwkIq6dLds50sb5CbBTof-e4JSENO9w9HV1UHolpI55QvxuC7mXGmRkzM0pUSLTJBcnf_rE3SV0ichVCitLtGEM8qoVnKK9If1u7oBHMDGpm62eNeGdhtttzviqo2YP-POxr72AXBVQyhxvbfbAbxGF5UNCW5OOUObl2KzfMtW69f35dMq84It-oxbXTpZEl4qL6hjpdPUcu9oBR68ZF4Qph2Xlmit7HCLSk2Fo8CstCznM3Q_znax_TpA6s2-Th5CsA20h2QYV0RylksyoA8j6mObUoTKdHE4G4-GEvMjyqwLM4oa2LvT7MHtofwjf83wb_RnYXA</recordid><startdate>20200203</startdate><enddate>20200203</enddate><creator>Shao, Siyao</creator><creator>Mallery, Kevin</creator><creator>Kumar, S Santosh</creator><creator>Hong, Jiarong</creator><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20200203</creationdate><title>Machine learning holography for 3D particle field imaging</title><author>Shao, Siyao ; Mallery, Kevin ; Kumar, S Santosh ; Hong, Jiarong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-3a9db5d03d7c41b2db91a3cb1fecec52c4029b35a0997a12115914b1e2a5a283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shao, Siyao</creatorcontrib><creatorcontrib>Mallery, Kevin</creatorcontrib><creatorcontrib>Kumar, S Santosh</creatorcontrib><creatorcontrib>Hong, Jiarong</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Optics express</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shao, Siyao</au><au>Mallery, Kevin</au><au>Kumar, S Santosh</au><au>Hong, Jiarong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine learning holography for 3D particle field imaging</atitle><jtitle>Optics express</jtitle><addtitle>Opt Express</addtitle><date>2020-02-03</date><risdate>2020</risdate><volume>28</volume><issue>3</issue><spage>2987</spage><epage>2999</epage><pages>2987-2999</pages><issn>1094-4087</issn><eissn>1094-4087</eissn><abstract>We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate measurement of individual particles is crucial. Assessments on both synthetic and experimental holograms demonstrate a significant improvement in particle extraction rate, localization accuracy and speed compared to prior methods over a wide range of particle concentrations, including highly dense concentrations where other methods are unsuitable. Our approach can be potentially extended to other types of computational imaging tasks with similar features.</abstract><cop>United States</cop><pmid>32121975</pmid><doi>10.1364/OE.379480</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1094-4087
ispartof Optics express, 2020-02, Vol.28 (3), p.2987-2999
issn 1094-4087
1094-4087
language eng
recordid cdi_proquest_miscellaneous_2370532850
source EZB Electronic Journals Library
title Machine learning holography for 3D particle field imaging
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T14%3A44%3A13IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Machine%20learning%20holography%20for%203D%20particle%20field%20imaging&rft.jtitle=Optics%20express&rft.au=Shao,%20Siyao&rft.date=2020-02-03&rft.volume=28&rft.issue=3&rft.spage=2987&rft.epage=2999&rft.pages=2987-2999&rft.issn=1094-4087&rft.eissn=1094-4087&rft_id=info:doi/10.1364/OE.379480&rft_dat=%3Cproquest_cross%3E2370532850%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c426t-3a9db5d03d7c41b2db91a3cb1fecec52c4029b35a0997a12115914b1e2a5a283%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2370532850&rft_id=info:pmid/32121975&rfr_iscdi=true