Boundary-Preserved Deep Denoising of Stochastic Resonance Enhanced Multiphoton Images

Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily s...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE journal of translational engineering in health and medicine Vol. 10; p. 1
Main Authors:	Niu, Sheng-Yong, Guo, Lun-Zhang, Li, Yue, Zhang, Zhiming, Wang, Tzung-Dau, Liu, Kai-Chun, Li, You-Jin, Tsao, Yu, Liu, Tzu-Ming
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks Background noise Boundaries Deep Denoising Autoencoder Fluorescence Harmonic generations Image acquisition Image contrast Image enhancement Image quality Image segmentation Imaging Microprocessors Microscopy Noise reduction Optical filters Photonics Photons Signal to noise ratio Similarity Stochastic resonance Third harmonic generation Three-photon fluorescence
ISSN:	2168-2372, 2168-2372
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress the perturbative noise in high-contrast images; however, for low photon budget multiphoton images, a high detector gain will not only boost the signals but also bring significant background noise. In such a stochastic resonance imaging regime, subthreshold signals may be detectable with the help of noise, meaning that a denoising filter capable of removing noise without sacrificing important cellular features, such as cell boundaries, is desirable. Method: We propose a convolutional neural network-based denoising autoencoder method - a fully convolutional deep denoising autoencoder (DDAE) - to improve the quality of three-photon fluorescence (3PF) and third-harmonic generation (THG) microscopy images. Results: The average of 200 acquired images of a given location served as the low-noise answer for the DDAE training. Compared with other conventional denoising methods, our DDAE model shows a better signal-to-noise ratio (28.86 and 21.66 for 3PF and THG, respectively), structural similarity (0.89 and 0.70 for 3PF and THG, respectively), and preservation of the nuclear or cellular boundaries (F1-score of 0.662 and 0.736 for 3PF and THG, respectively). It shows that DDAE is a better trade-off approach between structural similarity and preserving signal regions. Conclusions: The results of this study validate the effectiveness of the DDAE system in boundary-preserved image denoising. Clinical Impact: The proposed deep denoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment.
AbstractList	Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress the perturbative noise in high-contrast images; however, for low photon budget multiphoton images, a high detector gain will not only boost the signals but also bring significant background noise. In such a stochastic resonance imaging regime, subthreshold signals may be detectable with the help of noise, meaning that a denoising filter capable of removing noise without sacrificing important cellular features, such as cell boundaries, is desirable. Method: We propose a convolutional neural network-based denoising autoencoder method - a fully convolutional deep denoising autoencoder (DDAE) - to improve the quality of three-photon fluorescence (3PF) and third-harmonic generation (THG) microscopy images. Results: The average of 200 acquired images of a given location served as the low-noise answer for the DDAE training. Compared with other conventional denoising methods, our DDAE model shows a better signal-to-noise ratio (28.86 and 21.66 for 3PF and THG, respectively), structural similarity (0.89 and 0.70 for 3PF and THG, respectively), and preservation of the nuclear or cellular boundaries (F1-score of 0.662 and 0.736 for 3PF and THG, respectively). It shows that DDAE is a better trade-off approach between structural similarity and preserving signal regions. Conclusions: The results of this study validate the effectiveness of the DDAE system in boundary-preserved image denoising. Clinical Impact: The proposed deep denoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment. With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress the perturbative noise in high-contrast images; however, for low photon budget multiphoton images, a high detector gain will not only boost the signals but also bring significant background noise. In such a stochastic resonance imaging regime, subthreshold signals may be detectable with the help of noise, meaning that a denoising filter capable of removing noise without sacrificing important cellular features, such as cell boundaries, is desirable.OBJECTIVEWith the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to retain morphological features for further texture analysis and segmentation. Conventional denoising filters and models can easily suppress the perturbative noise in high-contrast images; however, for low photon budget multiphoton images, a high detector gain will not only boost the signals but also bring significant background noise. In such a stochastic resonance imaging regime, subthreshold signals may be detectable with the help of noise, meaning that a denoising filter capable of removing noise without sacrificing important cellular features, such as cell boundaries, is desirable.We propose a convolutional neural network-based denoising autoencoder method - a fully convolutional deep denoising autoencoder (DDAE) - to improve the quality of three-photon fluorescence (3PF) and third-harmonic generation (THG) microscopy images.METHODWe propose a convolutional neural network-based denoising autoencoder method - a fully convolutional deep denoising autoencoder (DDAE) - to improve the quality of three-photon fluorescence (3PF) and third-harmonic generation (THG) microscopy images.The average of 200 acquired images of a given location served as the low-noise answer for the DDAE training. Compared with other conventional denoising methods, our DDAE model shows a better signal-to-noise ratio (28.86 and 21.66 for 3PF and THG, respectively), structural similarity (0.89 and 0.70 for 3PF and THG, respectively), and preservation of the nuclear or cellular boundaries (F1-score of 0.662 and 0.736 for 3PF and THG, respectively). It shows that DDAE is a better trade-off approach between structural similarity and preserving signal regions.RESULTSThe average of 200 acquired images of a given location served as the low-noise answer for the DDAE training. Compared with other conventional denoising methods, our DDAE model shows a better signal-to-noise ratio (28.86 and 21.66 for 3PF and THG, respectively), structural similarity (0.89 and 0.70 for 3PF and THG, respectively), and preservation of the nuclear or cellular boundaries (F1-score of 0.662 and 0.736 for 3PF and THG, respectively). It shows that DDAE is a better trade-off approach between structural similarity and preserving signal regions.The results of this study validate the effectiveness of the DDAE system in boundary-preserved image denoising.CONCLUSIONSThe results of this study validate the effectiveness of the DDAE system in boundary-preserved image denoising.The proposed deep denoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment.CLINICAL IMPACTThe proposed deep denoising system can enhance the quality of microscopic images and effectively support clinical evaluation and assessment.
Author	Guo, Lun-Zhang Zhang, Zhiming Liu, Kai-Chun Li, You-Jin Liu, Tzu-Ming Niu, Sheng-Yong Li, Yue Tsao, Yu Wang, Tzung-Dau
AuthorAffiliation	Department of Electrical Engineering Chung Yuan Christian University 34900 Taoyuan 32023 Taiwan Institute of Translational Medicine, Faculty of Health Sciences & Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Taipa 59193 Macau China Research Center for Information Technology Innovation (CITI) Academia Sinica 38017 Taipei 11529 Taiwan Cardiovascular Center and Division of Cardiology Department of Internal Medicine College of Medicine, National Taiwan University Hospital 38006 Taipei 10002 Taiwan Department of Computer Science and Engineering University of California San Diego 8784 San Diego CA 92093 USA Department of Biomedical Engineering National Taiwan University 33561 Taipei 10617 Taiwan
AuthorAffiliation_xml	– name: Department of Biomedical Engineering National Taiwan University 33561 Taipei 10617 Taiwan – name: Department of Electrical Engineering Chung Yuan Christian University 34900 Taoyuan 32023 Taiwan – name: Cardiovascular Center and Division of Cardiology Department of Internal Medicine College of Medicine, National Taiwan University Hospital 38006 Taipei 10002 Taiwan – name: Department of Computer Science and Engineering University of California San Diego 8784 San Diego CA 92093 USA – name: Institute of Translational Medicine, Faculty of Health Sciences & Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Taipa 59193 Macau China – name: Research Center for Information Technology Innovation (CITI) Academia Sinica 38017 Taipei 11529 Taiwan
Author_xml	– sequence: 1 givenname: Sheng-Yong surname: Niu fullname: Niu, Sheng-Yong organization: Research Center for Information Technology Innovation (CITI), Academia Sinica, Taipei, Taiwan – sequence: 2 givenname: Lun-Zhang surname: Guo fullname: Guo, Lun-Zhang organization: Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan – sequence: 3 givenname: Yue surname: Li fullname: Li, Yue organization: Faculty of Health Sciences & Ministry of Education Frontiers Science Center for Precision Oncology, Institute of Translational Medicine, University of Macau, Macao SAR, China – sequence: 4 givenname: Zhiming surname: Zhang fullname: Zhang, Zhiming organization: Faculty of Health Sciences & Ministry of Education Frontiers Science Center for Precision Oncology, Institute of Translational Medicine, University of Macau, Macao SAR, China – sequence: 5 givenname: Tzung-Dau orcidid: 0000-0002-7180-3607 surname: Wang fullname: Wang, Tzung-Dau organization: Department of Internal Medicine, Cardiovascular Center and Division of Cardiology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan – sequence: 6 givenname: Kai-Chun orcidid: 0000-0001-7867-4716 surname: Liu fullname: Liu, Kai-Chun organization: Research Center for Information Technology Innovation (CITI), Academia Sinica, Taipei, Taiwan – sequence: 7 givenname: You-Jin surname: Li fullname: Li, You-Jin organization: Research Center for Information Technology Innovation (CITI), Academia Sinica, Taipei, Taiwan – sequence: 8 givenname: Yu orcidid: 0000-0001-6956-0418 surname: Tsao fullname: Tsao, Yu organization: Research Center for Information Technology Innovation (CITI), Academia Sinica, Taipei, Taiwan – sequence: 9 givenname: Tzu-Ming surname: Liu fullname: Liu, Tzu-Ming organization: Faculty of Health Sciences & Ministry of Education Frontiers Science Center for Precision Oncology, Institute of Translational Medicine, University of Macau, Macao SAR, China
BookMark	eNp9UU1vEzEUtFCRKKF_AC4rceGS4K_98AUJStoGtQJBe7be2s-Jo40d7N1K_Ht2m4BoD_jgZz3PjN94XpKTEAMS8prRBWNUvf9yu7y6WXDK-UJwWsmmeUZOOauaORc1P_nn_IKc5byl42pYpbg6JXef4hAspF_zbwkzpnu0xWfE_biF6LMP6yK64kcfzQZy703xHXMMEAwWy7CZqi1uhq73-03sYyhWO1hjfkWeO-gynh3rjNxdLG_Pr-bXXy9X5x-v50Yq0c-No4rauq0sb01TybJi0DrBleQWRGWFEQiydMKqEoC1wjVGMGcrpzjImosZWR10bYSt3ie_G53oCF4_NGJaa0jj1B1qWwNthRKyda1UyFpHpSxBudoYIYwdtT4ctPZDu0NrMPQJukeij2-C3-h1vNeqVJyOfmbk3VEgxZ8D5l7vfDbYdRAwDlnzWlDBWM0n6Nsn0G0cUhi_akRxXkqlqsldc0CZFHNO6LTxPfQ-Tu_7TjOqp_z1Q_56yl8f8x-p_An1j4__kt4cSB4R_xJU06iaMvEb40O_Rg
CODEN	IJTEBN
CitedBy_id	crossref_primary_10_1088_1361_6420_ad3c67
Cites_doi	10.1038/srep37210 10.1038/s41467-020-14929-2 10.1109/TIP.2003.819861 10.1038/sj.cgt.7700244 10.1364/OE.11.003093 10.1364/OL.26.001909 10.1016/j.optcom.2017.09.041 10.1117/12.643267 10.1109/TIP.2010.2056693 10.1038/s41592-018-0216-7 10.1364/BOE.3.001455 10.1242/jcs.152272 10.1111/jmi.12270 10.1242/jcs.033837 10.1073/pnas.1018743108 10.1117/3.353798 10.1109/ACCESS.2019.2912036 10.1364/BOE.7.002671 10.1038/nature06293 10.1038/s41467-017-02481-5 10.1109/CLEOE-IQEC.2013.6801501 10.1109/ICASSP.2014.6854807 10.1109/TIP.2017.2662206 10.1186/1471-2105-11-558 10.1016/j.cell.2018.08.028 10.1109/CVPR.2019.01198 10.1109/TASLP.2018.2821903 10.1158/2159-8290.CD-15-0012 10.1007/978-3-319-24574-4_28 10.1109/TIP.2010.2073477 10.1002/sca.4950100403 10.1103/PhysRevLett.78.1186 10.5120/8295-1830 10.1038/nature12354 10.1016/b978-0-12-668330-1.50007-7 10.1126/science.2321027 10.1364/BOE.3.002860 10.1016/j.bpj.2009.10.035 10.1038/nprot.2010.168 10.1038/s41598-019-51093-0
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 2022 Author
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 – notice: 2022 Author
DBID	97E ESBDL RIA RIE AAYXX CITATION 8FD F28 FR3 K9. 7X8 5PM DOA
DOI	10.1109/JTEHM.2022.3206488
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Xplore Digital Library CrossRef Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database ProQuest Health & Medical Complete (Alumni) MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef ProQuest Health & Medical Complete (Alumni) Engineering Research Database Technology Research Database ANTE: Abstracts in New Technology & Engineering MEDLINE - Academic
DatabaseTitleList	ProQuest Health & Medical Complete (Alumni) MEDLINE - Academic
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Xplore Digital Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	2168-2372
EndPage	1
ExternalDocumentID	oai_doaj_org_article_d7a0b3934bfb49e1bf0445a9f7cc33cd PMC9592049 10_1109_JTEHM_2022_3206488 9889701
Genre	orig-research
GrantInformation_xml	– fundername: Ministry of Science and Technology grantid: MOST 107-2314-B-002-262-MY2 funderid: 10.13039/100007225 – fundername: Academia Sinica grantid: AS-GC-111-M01 funderid: 10.13039/501100001869 – fundername: The Science and Technology Development Fund, Macau SAR grantid: 122/2016/A3, 018/2017/A1, 0011/2019/AKP, 0120/2020 – fundername: ; – fundername: ; grantid: MOST 107-2314-B-002-262-MY2 – fundername: ; grantid: 122/2016/A3; 018/2017/A1; 0011/2019/AKP; 0120/2020/A3; 0026/2021/A – fundername: ; grantid: AS-GC-111-M01
GroupedDBID	0R~ 53G 5VS 6IK 97E AAJGR ABVLG ACGFS ADBBV ADRAZ AGSQL ALMA_UNASSIGNED_HOLDINGS AOIJS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ DIK EBS EJD ESBDL GROUPED_DOAJ HYE IPLJI JAVBF KQ8 M43 M48 M~E O9- OCL OK1 PGMZT RIA RIE RPM AAYXX CITATION 8FD ABAZT F28 FR3 K9. 7X8 5PM
ID	FETCH-LOGICAL-c493t-cf090d7b6d2bc864561abf32942da36d3c3ea45f3d95aa1b3f8c31fd6f92a4723
IEDL.DBID	DOA
ISICitedReferencesCount	1
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000865085100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2168-2372
IngestDate	Fri Oct 03 12:39:09 EDT 2025 Thu Aug 21 18:38:59 EDT 2025 Sun Nov 09 09:22:10 EST 2025 Tue Oct 07 06:58:31 EDT 2025 Tue Nov 18 22:41:19 EST 2025 Sat Nov 29 03:57:10 EST 2025 Tue Nov 25 14:44:25 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c493t-cf090d7b6d2bc864561abf32942da36d3c3ea45f3d95aa1b3f8c31fd6f92a4723
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-7180-3607 0000-0001-6956-0418 0000-0001-7867-4716
OpenAccessLink	https://doaj.org/article/d7a0b3934bfb49e1bf0445a9f7cc33cd
PQID	2722549962
PQPubID	4437232
PageCount	1
ParticipantIDs	crossref_citationtrail_10_1109_JTEHM_2022_3206488 proquest_miscellaneous_2730311729 crossref_primary_10_1109_JTEHM_2022_3206488 proquest_journals_2722549962 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9592049 doaj_primary_oai_doaj_org_article_d7a0b3934bfb49e1bf0445a9f7cc33cd ieee_primary_9889701
PublicationCentury	2000
PublicationDate	2022-01-01
PublicationDateYYYYMMDD	2022-01-01
PublicationDate_xml	– month: 01 year: 2022 text: 2022-01-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE journal of translational engineering in health and medicine
PublicationTitleAbbrev	JTEHM
PublicationYear	2022
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref13 ref35 ref12 ref34 ref15 ref37 ref14 ref36 ref31 ref11 ref33 ref10 ref32 ref2 ref1 ref17 ref39 ref16 ref38 ref19 ref18 Tsai (ref22) ref24 ref23 Lehtinen (ref44) 2018 ref26 ref25 ref20 ref42 ref41 ref21 ref43 ref28 ref27 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 Krizhevsky (ref30); 25
References_xml	– ident: ref12 doi: 10.1038/srep37210 – ident: ref14 doi: 10.1038/s41467-020-14929-2 – ident: ref38 doi: 10.1109/TIP.2003.819861 – ident: ref4 doi: 10.1038/sj.cgt.7700244 – ident: ref20 doi: 10.1364/OE.11.003093 – ident: ref17 doi: 10.1364/OL.26.001909 – ident: ref9 doi: 10.1016/j.optcom.2017.09.041 – ident: ref26 doi: 10.1117/12.643267 – ident: ref25 doi: 10.1109/TIP.2010.2056693 – ident: ref33 doi: 10.1038/s41592-018-0216-7 – ident: ref13 doi: 10.1364/BOE.3.001455 – ident: ref23 doi: 10.1242/jcs.152272 – ident: ref29 doi: 10.1111/jmi.12270 – ident: ref2 doi: 10.1242/jcs.033837 – ident: ref21 doi: 10.1073/pnas.1018743108 – ident: ref37 doi: 10.1117/3.353798 – ident: ref36 doi: 10.1109/ACCESS.2019.2912036 – ident: ref40 doi: 10.1364/BOE.7.002671 – ident: ref7 doi: 10.1038/nature06293 – ident: ref5 doi: 10.1038/s41467-017-02481-5 – ident: ref18 doi: 10.1109/CLEOE-IQEC.2013.6801501 – ident: ref34 doi: 10.1109/ICASSP.2014.6854807 – start-page: 2317 volume-title: Proc. OSA ident: ref22 article-title: In vivo virtual biopsy of human skin by using noninvasive higher harmonic generation microscopy – ident: ref32 doi: 10.1109/TIP.2017.2662206 – ident: ref43 doi: 10.1186/1471-2105-11-558 – ident: ref10 doi: 10.1016/j.cell.2018.08.028 – ident: ref39 doi: 10.1109/CVPR.2019.01198 – ident: ref35 doi: 10.1109/TASLP.2018.2821903 – ident: ref19 doi: 10.1038/srep37210 – year: 2018 ident: ref44 article-title: Noise2Noise: Learning image restoration without clean data publication-title: arXiv:1803.04189 – ident: ref6 doi: 10.1158/2159-8290.CD-15-0012 – ident: ref31 doi: 10.1007/978-3-319-24574-4_28 – ident: ref41 doi: 10.1109/TIP.2010.2073477 – ident: ref15 doi: 10.1002/sca.4950100403 – ident: ref24 doi: 10.1103/PhysRevLett.78.1186 – ident: ref27 doi: 10.5120/8295-1830 – ident: ref8 doi: 10.1038/nature12354 – volume: 25 start-page: 1097 volume-title: Proc. Adv. Neural Inf. Process. Syst. (NIPS) ident: ref30 article-title: ImageNet classification with deep convolutional neural networks – ident: ref42 doi: 10.1016/b978-0-12-668330-1.50007-7 – ident: ref1 doi: 10.1126/science.2321027 – ident: ref11 doi: 10.1364/BOE.3.002860 – ident: ref16 doi: 10.1016/j.bpj.2009.10.035 – ident: ref3 doi: 10.1038/nprot.2010.168 – ident: ref28 doi: 10.1038/s41598-019-51093-0
SSID	ssj0000816929
Score	2.2138784
Snippet	Objective: With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising... With the rapid growth of high-speed deep-tissue imaging in biomedical research, there is an urgent need to develop a robust and effective denoising method to...
SourceID	doaj pubmedcentral proquest crossref ieee
SourceType	Open Website Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	1
SubjectTerms	Artificial neural networks Background noise Boundaries Deep Denoising Autoencoder Fluorescence Harmonic generations Image acquisition Image contrast Image enhancement Image quality Image segmentation Imaging Microprocessors Microscopy Noise reduction Optical filters Photonics Photons Signal to noise ratio Similarity Stochastic resonance Third harmonic generation Three-photon fluorescence
SummonAdditionalLinks	– databaseName: IEEE Xplore Digital Library dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1La9wwEB7S0EN76CstdZIWF3prnciSbEnHJt2QFhoKTSA3oye7kNrLPgr599FovU4MpdCLMZZsZI_GmhnNfB_ARxdoKF30VOtAecGdMYX0FSksDxyxQSpRhkQ2IS4u5PW1-rkDn4daGO99Sj7zR3ia9vJdZ9cYKjtWUiqBxVqPhKg3tVpDPAUJJOJSv62LIer4--Xk_Ef0ACk9YjSuvIlc5X7tSRD9PafKyLwcJ0c-WG3Onv_fOF_As96qzL9spsFL2PHtK3j6AGtwD65OEoHS4rbArAvMc3T5V-_n8dB2M4wY5F3If606O9WI3ZxjYB_ROHw-aacpTyBP1brzaRftxfzb7_gnWr6Gq7PJ5el50XMqxK-v2KqwgSjihKkdNVbWaD5pExhVnDrNascs85pXgTlVaV0aFqRlZXB1UFRzQdkb2G271r-FvJJSh0oKGTzhWkjNDVG69sQyjvRlGZTbr93YHnAceS9umuR4ENUkCTUooaaXUAafhnvmG7iNf_Y-QSEOPREqO12I0mh6zWuc0MQwxbgJhitfmkA4r7QKwlrGrMtgDyU4PKQXXgaH2ynR9Gq9bKigyaGuaQYfhuaokLjLolvfrbFPtArKaBeqDMRoKo3GOW5pZ9ME7a0qRaPPtv_3MR3AE3z9TQToEHZXi7V_B4_tn9VsuXif9OEOv6QL8Q priority: 102 providerName: IEEE
Title	Boundary-Preserved Deep Denoising of Stochastic Resonance Enhanced Multiphoton Images
URI	https://ieeexplore.ieee.org/document/9889701 https://www.proquest.com/docview/2722549962 https://www.proquest.com/docview/2730311729 https://pubmed.ncbi.nlm.nih.gov/PMC9592049 https://doaj.org/article/d7a0b3934bfb49e1bf0445a9f7cc33cd
Volume	10
WOSCitedRecordID	wos000865085100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2168-2372 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816929 issn: 2168-2372 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2168-2372 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816929 issn: 2168-2372 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB1BxQEOFVAQKe0qSNxQqGM7sX2kdKuC1AqJVtpb5E_tSpCsdrdIXPjteJzssrnAhYsPsRPZ47Fnxhm_B_DWBRpKFyPVOlBecGdMIX1FCssDR2yQSpQhkU2Imxs5m6kve1RfmBPWwwP3gjtzQhPDFOMmGK58aQLhvNIqCGsZsw53XyLUXjCV9mBZ1tHwb2_JEHX2-XZ6dR3jQUrfMxrtcKJa-WOJEmD_wLAycjbHqZJ7tufyKRwOTmP-oe_sM3jg2-fwZA9K8AjuzhM_0upngUkVmMbo8gvvl7FouwUeCORdyL9uOjvXCM2c47k9gm34fNrOUxpAni7jLudddAfzT9_jRrN-AXeX09uPV8VAmRCFq9imsIEo4oSpHTVW1ugdaRMYVZw6zWrHLPOaV4E5VWldGhakZWVwdVBUc0HZSzhou9a_grySUodKChk84VpIzQ1RuvbEMo7sZBmUW_E1dsATR1qLb02KK4hqksgbFHkziDyDd7t3lj2axl9bn-Os7FoiEnZ6EPWjGfSj-Zd-ZHCEc7r7iJJSCVJmcLKd42ZYteuGCpri5Zpm8GZXHdcb_kTRre_usU00-mV0-1QGYqQbo36Oa9rFPCF3q0rRGJId_4-BvYbHKKz-OOgEDjare38Kj-yPzWK9msBDMZOTtChief1rOklXG38DPQgTBw
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3di9QwEA_HKeg9-HWK1VMr-Ka9S5O0SR493WNP7xbBPbi3kE92QdtlPw78781ku_UKIvhSSpOWkMk0M5OZ3w-hdy6QULroqdaBsII5YwrhK1xYFhhgg1S8DIlsgk8m4vpafttDH_paGO99Sj7zx3CbzvJdazcQKjuRQkgOxVp3KsYI3lZr9REVoJCIm_2uMgbLky_T0fgy-oCEHFMS995Er_Jn90kg_R2rysDAHKZH3tpvzh7-30gfoQedXZl_3C6Ex2jPN0_QwS20wUN0dZoolJa_Csi7gExHl3_2fhEvTTuHmEHehvz7urUzDejNOYT2AY_D56NmljIF8lSvu5i10WLMz3_Gf9HqKbo6G00_jYuOVSHOv6TrwgYsseOmdsRYUYMBpU2gRDLiNK0dtdRrVgXqZKV1aWgQlpbB1UESzTihz9B-0zb-OcorIXSoBBfBY6a50MxgqWuPLWVAYJahcjfbynaQ48B88UMl1wNLlSSkQEKqk1CG3vfvLLaAG__sfQpC7HsCWHZ6EKWhOt1TjmtsqKTMBMOkL03AjFVaBm4tpdZl6BAk2H-kE16GjnZLQnWKvVKEk-RS1yRDb_vmqJJwzqIb326gT7QLymgZygzxwVIajHPY0sxnCdxbVpJEr-3F38f0Bt0bTy8v1MX55OtLdB-mYhsPOkL76-XGv0J37c16vlq-TrrxG8ZyDzg
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Boundary-Preserved+Deep+Denoising+of+Stochastic+Resonance+Enhanced+Multiphoton+Images&rft.jtitle=IEEE+journal+of+translational+engineering+in+health+and+medicine&rft.au=Niu%2C+Sheng-Yong&rft.au=Guo%2C+Lun-Zhang&rft.au=Li%2C+Yue&rft.au=Zhang%2C+Zhiming&rft.date=2022-01-01&rft.issn=2168-2372&rft.eissn=2168-2372&rft.volume=10&rft.spage=1800812&rft_id=info:doi/10.1109%2FJTEHM.2022.3206488&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2372&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2372&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2372&client=summon