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Hybrid Space Calibrated 3D Network of Diffractive Hyperspectral Optical Imaging Sensor
Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network "HSC3D"...
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Published in: | Sensors (Basel, Switzerland) Switzerland), 2024-10, Vol.24 (21), p.6903 |
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description | Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network "HSC3D" for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing. |
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Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network "HSC3D" for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing.</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s24216903</identifier><identifier>PMID: 39517799</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Artificial intelligence ; Calibration ; diffractive lenses ; Light ; multispectral imaging ; Neural networks ; point spread function ; Research methodology ; Sensors ; space calibration ; Spectrum analysis</subject><ispartof>Sensors (Basel, Switzerland), 2024-10, Vol.24 (21), p.6903</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 by the authors. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c364t-63f6b1b5a610d3a4995ace10517f8d48d910a2cbf2ba9062959d69edf6e249ab3</cites><orcidid>0000-0002-9326-5547 ; 0000-0002-3216-526X ; 0000-0003-2097-7399</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3126285302/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3126285302?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39517799$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Fan, Hao</creatorcontrib><creatorcontrib>Li, Chenxi</creatorcontrib><creatorcontrib>Gao, Bo</creatorcontrib><creatorcontrib>Xu, Huangrong</creatorcontrib><creatorcontrib>Chen, Yuwei</creatorcontrib><creatorcontrib>Zhang, Xuming</creatorcontrib><creatorcontrib>Li, Xu</creatorcontrib><creatorcontrib>Yu, Weixing</creatorcontrib><title>Hybrid Space Calibrated 3D Network of Diffractive Hyperspectral Optical Imaging Sensor</title><title>Sensors (Basel, Switzerland)</title><addtitle>Sensors (Basel)</addtitle><description>Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. 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The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Calibration</subject><subject>diffractive lenses</subject><subject>Light</subject><subject>multispectral imaging</subject><subject>Neural networks</subject><subject>point spread function</subject><subject>Research methodology</subject><subject>Sensors</subject><subject>space calibration</subject><subject>Spectrum analysis</subject><issn>1424-8220</issn><issn>1424-8220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdUk1vEzEUXCEQbQMH_gBaiQscUvy1Hz6hKgUSqaKHAlfr2X5eHDbrrb0pyr-vQ0rUIh-e9Twez_hNUbyh5JxzST4mJhitJeHPilMqmJi3jJHnj_YnxVlKa0IY57x9WZxwWdGmkfK0-Lnc6ehteTOCwXIBvdcRJrQlvyy_4fQnxN9lcOWldy6CmfwdlsvdiDGNaKYIfXk9Tt7kutpA54euvMEhhfiqeOGgT_j6oc6KH18-f18s51fXX1eLi6u54bWY5jV3taa6gpoSy0FIWWUZlGR1rrWitZISYEY7pkGSmslK2lqidTUyIUHzWbE68NoAazVGv4G4UwG8-tsIsVMQs8AeFVCoBTjdUAui4UJz01qGBi0Xlasgc306cI1bvUFrcNgbfEL69GTwv1QX7hSllWg54Znh_QNDDLdbTJPa-GSw72HAsE2KU9Y2gjSEZui7_6DrsI1D_qs9qmZtxfO0ZsX5AdVBduAHF_LDJi-LG2_CgM7n_kWbBeQpy72CD4cLJoaUIrqjfErUPivqmJWMffvY7xH5Lxz8HobHuKw</recordid><startdate>20241028</startdate><enddate>20241028</enddate><creator>Fan, Hao</creator><creator>Li, Chenxi</creator><creator>Gao, Bo</creator><creator>Xu, Huangrong</creator><creator>Chen, Yuwei</creator><creator>Zhang, Xuming</creator><creator>Li, Xu</creator><creator>Yu, Weixing</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-9326-5547</orcidid><orcidid>https://orcid.org/0000-0002-3216-526X</orcidid><orcidid>https://orcid.org/0000-0003-2097-7399</orcidid></search><sort><creationdate>20241028</creationdate><title>Hybrid Space Calibrated 3D Network of Diffractive Hyperspectral Optical Imaging Sensor</title><author>Fan, Hao ; 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subjects | Accuracy Algorithms Artificial intelligence Calibration diffractive lenses Light multispectral imaging Neural networks point spread function Research methodology Sensors space calibration Spectrum analysis |
title | Hybrid Space Calibrated 3D Network of Diffractive Hyperspectral Optical Imaging Sensor |
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