Loading…

Real-time wavefront correction using diffractive optical networks

Real-time wavefront correction is a challenging problem to present for conventional adaptive optics systems. Here, we present an all-optical system to realize real-time wavefront correction. Using deep learning, the system, which contains only multiple transmissive diffractive layers, is trained to...

Full description

Saved in:
Bibliographic Details
Published in:Optics express 2023-01, Vol.31 (2), p.1067-1078
Main Authors: Pan, Xiushan, Zuo, Heng, Bai, Hua, Wu, Zhixu, Cui, Xiangqun
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-c250t-a6d7f3e27439c8101d47f278e6bd5fc7dc531867a40d1de0dca67f42e67c18283
cites cdi_FETCH-LOGICAL-c250t-a6d7f3e27439c8101d47f278e6bd5fc7dc531867a40d1de0dca67f42e67c18283
container_end_page 1078
container_issue 2
container_start_page 1067
container_title Optics express
container_volume 31
creator Pan, Xiushan
Zuo, Heng
Bai, Hua
Wu, Zhixu
Cui, Xiangqun
description Real-time wavefront correction is a challenging problem to present for conventional adaptive optics systems. Here, we present an all-optical system to realize real-time wavefront correction. Using deep learning, the system, which contains only multiple transmissive diffractive layers, is trained to realize high-quality imaging for unknown, random, distorted wavefronts. Once physically fabricated, this passive optical system is physically positioned between the imaging lens and the image plane to all-optically correct unknown, new wavefronts whose wavefront errors are within the training range. Simulated experiments showed that the system designed for the on-axis field of view increases the average imaging Strehl Ratio from 0.32 to 0.94, and the other system intended for multiple fields of view increases the resolvable probability of binary stars from 30.5% to 69.5%. Results suggested that DAOS performed well when performing wavefront correction at the speed of light. The solution of real-time wavefront correction can be applied to other wavelengths and has great application potential in astronomical observation, laser communication, and other fields.
doi_str_mv 10.1364/OE.478492
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2776516545</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2776516545</sourcerecordid><originalsourceid>FETCH-LOGICAL-c250t-a6d7f3e27439c8101d47f278e6bd5fc7dc531867a40d1de0dca67f42e67c18283</originalsourceid><addsrcrecordid>eNpNkE1LAzEURYMotlYX_gGZpS5Gk0wmL7MspX5AoSC6DmnyIqMzk5rMtPjvrbSKq3u5HO7iEHLJ6C0rpLhbzm8FKFHxIzJmtBK5oAqO__UROUvpnVImoIJTMiokqJKJakymz2iavK9bzLZmgz6Grs9siBFtX4cuG1LdvWWu9j6a3bLBLKz72pom67DfhviRzsmJN03Ci0NOyOv9_GX2mC-WD0-z6SK3vKR9bqQDXyAHUVRWMcqcAM9BoVy50ltwtiyYkmAEdcwhddZI8IKjBMsUV8WEXO9_1zF8Dph63dbJYtOYDsOQNAeQJZOlKHfozR61MaQU0et1rFsTvzSj-seYXs713tiOvTrcDqsW3R_5q6j4BjkMZhM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2776516545</pqid></control><display><type>article</type><title>Real-time wavefront correction using diffractive optical networks</title><source>EZB Electronic Journals Library</source><creator>Pan, Xiushan ; Zuo, Heng ; Bai, Hua ; Wu, Zhixu ; Cui, Xiangqun</creator><creatorcontrib>Pan, Xiushan ; Zuo, Heng ; Bai, Hua ; Wu, Zhixu ; Cui, Xiangqun</creatorcontrib><description>Real-time wavefront correction is a challenging problem to present for conventional adaptive optics systems. Here, we present an all-optical system to realize real-time wavefront correction. Using deep learning, the system, which contains only multiple transmissive diffractive layers, is trained to realize high-quality imaging for unknown, random, distorted wavefronts. Once physically fabricated, this passive optical system is physically positioned between the imaging lens and the image plane to all-optically correct unknown, new wavefronts whose wavefront errors are within the training range. Simulated experiments showed that the system designed for the on-axis field of view increases the average imaging Strehl Ratio from 0.32 to 0.94, and the other system intended for multiple fields of view increases the resolvable probability of binary stars from 30.5% to 69.5%. Results suggested that DAOS performed well when performing wavefront correction at the speed of light. The solution of real-time wavefront correction can be applied to other wavelengths and has great application potential in astronomical observation, laser communication, and other fields.</description><identifier>ISSN: 1094-4087</identifier><identifier>EISSN: 1094-4087</identifier><identifier>DOI: 10.1364/OE.478492</identifier><identifier>PMID: 36785149</identifier><language>eng</language><publisher>United States</publisher><ispartof>Optics express, 2023-01, Vol.31 (2), p.1067-1078</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c250t-a6d7f3e27439c8101d47f278e6bd5fc7dc531867a40d1de0dca67f42e67c18283</citedby><cites>FETCH-LOGICAL-c250t-a6d7f3e27439c8101d47f278e6bd5fc7dc531867a40d1de0dca67f42e67c18283</cites><orcidid>0000-0002-3638-9906</orcidid></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/36785149$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pan, Xiushan</creatorcontrib><creatorcontrib>Zuo, Heng</creatorcontrib><creatorcontrib>Bai, Hua</creatorcontrib><creatorcontrib>Wu, Zhixu</creatorcontrib><creatorcontrib>Cui, Xiangqun</creatorcontrib><title>Real-time wavefront correction using diffractive optical networks</title><title>Optics express</title><addtitle>Opt Express</addtitle><description>Real-time wavefront correction is a challenging problem to present for conventional adaptive optics systems. Here, we present an all-optical system to realize real-time wavefront correction. Using deep learning, the system, which contains only multiple transmissive diffractive layers, is trained to realize high-quality imaging for unknown, random, distorted wavefronts. Once physically fabricated, this passive optical system is physically positioned between the imaging lens and the image plane to all-optically correct unknown, new wavefronts whose wavefront errors are within the training range. Simulated experiments showed that the system designed for the on-axis field of view increases the average imaging Strehl Ratio from 0.32 to 0.94, and the other system intended for multiple fields of view increases the resolvable probability of binary stars from 30.5% to 69.5%. Results suggested that DAOS performed well when performing wavefront correction at the speed of light. The solution of real-time wavefront correction can be applied to other wavelengths and has great application potential in astronomical observation, laser communication, and other fields.</description><issn>1094-4087</issn><issn>1094-4087</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNpNkE1LAzEURYMotlYX_gGZpS5Gk0wmL7MspX5AoSC6DmnyIqMzk5rMtPjvrbSKq3u5HO7iEHLJ6C0rpLhbzm8FKFHxIzJmtBK5oAqO__UROUvpnVImoIJTMiokqJKJakymz2iavK9bzLZmgz6Grs9siBFtX4cuG1LdvWWu9j6a3bLBLKz72pom67DfhviRzsmJN03Ci0NOyOv9_GX2mC-WD0-z6SK3vKR9bqQDXyAHUVRWMcqcAM9BoVy50ltwtiyYkmAEdcwhddZI8IKjBMsUV8WEXO9_1zF8Dph63dbJYtOYDsOQNAeQJZOlKHfozR61MaQU0et1rFsTvzSj-seYXs713tiOvTrcDqsW3R_5q6j4BjkMZhM</recordid><startdate>20230116</startdate><enddate>20230116</enddate><creator>Pan, Xiushan</creator><creator>Zuo, Heng</creator><creator>Bai, Hua</creator><creator>Wu, Zhixu</creator><creator>Cui, Xiangqun</creator><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-3638-9906</orcidid></search><sort><creationdate>20230116</creationdate><title>Real-time wavefront correction using diffractive optical networks</title><author>Pan, Xiushan ; Zuo, Heng ; Bai, Hua ; Wu, Zhixu ; Cui, Xiangqun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c250t-a6d7f3e27439c8101d47f278e6bd5fc7dc531867a40d1de0dca67f42e67c18283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pan, Xiushan</creatorcontrib><creatorcontrib>Zuo, Heng</creatorcontrib><creatorcontrib>Bai, Hua</creatorcontrib><creatorcontrib>Wu, Zhixu</creatorcontrib><creatorcontrib>Cui, Xiangqun</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>Pan, Xiushan</au><au>Zuo, Heng</au><au>Bai, Hua</au><au>Wu, Zhixu</au><au>Cui, Xiangqun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-time wavefront correction using diffractive optical networks</atitle><jtitle>Optics express</jtitle><addtitle>Opt Express</addtitle><date>2023-01-16</date><risdate>2023</risdate><volume>31</volume><issue>2</issue><spage>1067</spage><epage>1078</epage><pages>1067-1078</pages><issn>1094-4087</issn><eissn>1094-4087</eissn><abstract>Real-time wavefront correction is a challenging problem to present for conventional adaptive optics systems. Here, we present an all-optical system to realize real-time wavefront correction. Using deep learning, the system, which contains only multiple transmissive diffractive layers, is trained to realize high-quality imaging for unknown, random, distorted wavefronts. Once physically fabricated, this passive optical system is physically positioned between the imaging lens and the image plane to all-optically correct unknown, new wavefronts whose wavefront errors are within the training range. Simulated experiments showed that the system designed for the on-axis field of view increases the average imaging Strehl Ratio from 0.32 to 0.94, and the other system intended for multiple fields of view increases the resolvable probability of binary stars from 30.5% to 69.5%. Results suggested that DAOS performed well when performing wavefront correction at the speed of light. The solution of real-time wavefront correction can be applied to other wavelengths and has great application potential in astronomical observation, laser communication, and other fields.</abstract><cop>United States</cop><pmid>36785149</pmid><doi>10.1364/OE.478492</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-3638-9906</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1094-4087
ispartof Optics express, 2023-01, Vol.31 (2), p.1067-1078
issn 1094-4087
1094-4087
language eng
recordid cdi_proquest_miscellaneous_2776516545
source EZB Electronic Journals Library
title Real-time wavefront correction using diffractive optical networks
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T16%3A20%3A06IST&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=Real-time%20wavefront%20correction%20using%20diffractive%20optical%20networks&rft.jtitle=Optics%20express&rft.au=Pan,%20Xiushan&rft.date=2023-01-16&rft.volume=31&rft.issue=2&rft.spage=1067&rft.epage=1078&rft.pages=1067-1078&rft.issn=1094-4087&rft.eissn=1094-4087&rft_id=info:doi/10.1364/OE.478492&rft_dat=%3Cproquest_cross%3E2776516545%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c250t-a6d7f3e27439c8101d47f278e6bd5fc7dc531867a40d1de0dca67f42e67c18283%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2776516545&rft_id=info:pmid/36785149&rfr_iscdi=true