Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints

The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Photonics Ročník 11; číslo 2; s. 190
Hlavní autoři:	Guan, Lei, Dong, Jiawei, Li, Qianxi, Huang, Jijiang, Chen, Weining, Wang, Hao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.02.2024
Témata:	Algorithms Collaboration collaborative constraint dark light enhancement Datasets Deep learning fidelity term Illumination Image enhancement Image restoration Light Methods Modules Noise control Noise reduction Optimization Robustness self-encoding prior Semantics Teaching methods United States New Jersey
ISSN:	2304-6732, 2304-6732
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes.
AbstractList	The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes.
Audience	Academic
Author	Guan, Lei Dong, Jiawei Huang, Jijiang Chen, Weining Li, Qianxi Wang, Hao
Author_xml	– sequence: 1 givenname: Lei surname: Guan fullname: Guan, Lei – sequence: 2 givenname: Jiawei surname: Dong fullname: Dong, Jiawei – sequence: 3 givenname: Qianxi surname: Li fullname: Li, Qianxi – sequence: 4 givenname: Jijiang surname: Huang fullname: Huang, Jijiang – sequence: 5 givenname: Weining surname: Chen fullname: Chen, Weining – sequence: 6 givenname: Hao surname: Wang fullname: Wang, Hao
BookMark	eNplkV9vVCEQxYmpibX2A_hG4vOtDHD_Pda16ibb1MT2mQB3uMt6F1Zgm_jtS11jmggPDJMzPw45b8lZiAEJeQ_sSoiRfTxsY4nB2wzAOIORvSLnXDDZdL3gZy_qN-Qy5x2rawQxtPKc2M86_aQbP28LXe_1jM1N2OpgcY-h0Fss2zjRTzrjRGOgt8el-MOC9AcuriptnHyY6ffkY6KruCzaxKSLf8R6C7kk7UPJ78hrp5eMl3_PC_Lw5eZ-9a3Z3H1dr643jRW9KE1XvevO8cEy08reSAeym1oOwI3psWdtJ8BwdCNW_1rbFowBcAY0SNcKcUHWJ-4U9U4dkt_r9FtF7dWfRkyz0ql4u6BqK1uPDjoDTrJhHJB39RmODIzjE6usDyfWIcVfR8xF7eIxhWpf8VEAa8XQP6uuTqpZV6gPLtYv27on3HtbQ3K-9q_7QQKTTHZ1AE4DNsWcE7p_NoGp5yzVf1mKJwkLlHA
Cites_doi	10.1145/2808492.2808511 10.1080/01621459.2020.1847120 10.1109/TIP.2019.2910412 10.1007/s10489-021-02627-5 10.1080/10618562.2020.1789116 10.1109/CVPR52688.2022.01426 10.1109/83.557356 10.1109/TMM.2020.3037526 10.1007/s00170-021-06592-8 10.1109/FG.2018.00118 10.3390/app13063861 10.1109/CVPR52688.2022.00581 10.1007/s11263-023-01852-4 10.1609/aaai.v37i1.25130 10.1093/mnras/stac2272 10.1109/SITIS.2013.19 10.5201/ipol.2014.107 10.1145/3343031.3350983 10.1007/s10489-022-04013-1 10.1109/CVPR52688.2022.01553 10.1016/j.jvcir.2022.103712 10.1109/TCSVT.2021.3049940 10.1109/CVPR46437.2021.01042 10.1145/3394171.3413757 10.4310/CMS.2019.v17.n5.a11 10.1109/VCIP.2017.8305143 10.1016/j.rse.2020.111716 10.1109/TCSVT.2017.2728822 10.1007/s10489-020-02119-y 10.1109/TIP.2021.3050850 10.1186/s40537-019-0212-5 10.1364/JOSA.61.000001 10.1109/83.597272 10.1109/JSTARS.2021.3076198 10.1073/pnas.2109420119
ContentType	Journal Article
Copyright	COPYRIGHT 2024 MDPI AG 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.
Copyright_xml	– notice: COPYRIGHT 2024 MDPI AG – notice: 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.
DBID	AAYXX CITATION 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD 8FE 8FG 8FH ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO F28 FR3 GNUQQ H8D H8G HCIFZ JG9 JQ2 KR7 L7M LK8 L~C L~D M7P P5Z P62 P64 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS DOA
DOI	10.3390/photonics11020190
DatabaseName	CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC proquest PBSC-Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection ProQuest One ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database ProQuest Central Student Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Biological Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Materials Research Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File ProQuest One Applied & Life Sciences Engineered Materials Abstracts Natural Science Collection Biological Science Collection ProQuest Central (New) ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Aluminium Industry Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Technology Collection Ceramic Abstracts Biological Science Database Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Natural Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library Biotechnology Research Abstracts ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Corrosion Abstracts
DatabaseTitleList	CrossRef Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Applied Sciences
EISSN	2304-6732
ExternalDocumentID	oai_doaj_org_article_5f14a9f16b1f40898e2612b2e01bf2d0 A784104046 10_3390_photonics11020190
GeographicLocations	United States New Jersey
GeographicLocations_xml	– name: New Jersey – name: United States
GroupedDBID	5VS 8FE 8FG 8FH AADQD AAFWJ AAYXX ABHFT ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS ARCSS BBNVY BCNDV BENPR BGLVJ BHPHI CCPQU CITATION GROUPED_DOAJ GX1 HCIFZ IAO ITC KQ8 KZ1 LK8 LMP M7P MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PROAC 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7U5 8BQ 8FD ABUWG AZQEC DWQXO F28 FR3 GNUQQ GS5 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c373t-6020a6f28c0b547b4f146d52112bb7e705631b2ef9e009aac51bb11fb1a14f533
IEDL.DBID	BENPR
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001172736100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2304-6732
IngestDate	Mon Nov 10 04:30:45 EST 2025 Sun Jul 13 05:23:35 EDT 2025 Sat Nov 29 10:29:32 EST 2025 Sat Nov 29 07:15:58 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	2
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c373t-6020a6f28c0b547b4f146d52112bb7e705631b2ef9e009aac51bb11fb1a14f533
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
OpenAccessLink	https://www.proquest.com/docview/2931053870?pq-origsite=%requestingapplication%
PQID	2931053870
PQPubID	2032352
ParticipantIDs	doaj_primary_oai_doaj_org_article_5f14a9f16b1f40898e2612b2e01bf2d0 proquest_journals_2931053870 gale_infotracacademiconefile_A784104046 crossref_primary_10_3390_photonics11020190
PublicationCentury	2000
PublicationDate	2024-02-01
PublicationDateYYYYMMDD	2024-02-01
PublicationDate_xml	– month: 02 year: 2024 text: 2024-02-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Photonics
PublicationYear	2024
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Huang (ref_36) 2021; 14 Wang (ref_14) 2018; 28 Hai (ref_18) 2023; 90 Yang (ref_22) 2021; 30 Wu (ref_7) 2022; 52 E (ref_35) 2019; 17 ref_13 ref_11 Li (ref_19) 2021; 31 ref_33 Ren (ref_25) 2019; 28 ref_32 ref_31 Zheng (ref_2) 2021; 113 Land (ref_8) 1971; 61 Jobson (ref_10) 1997; 6 ref_17 Rotskoff (ref_37) 2022; 119 ref_16 Jobson (ref_9) 1997; 6 Sreenu (ref_3) 2019; 6 Yuan (ref_4) 2020; 241 Chen (ref_30) 2023; 132 ref_24 ref_23 ref_21 ref_20 ref_1 Karamanis (ref_38) 2022; 516 ref_29 ref_28 ref_27 ref_26 Nemeth (ref_39) 2021; 116 Prat (ref_34) 2020; 34 Tan (ref_6) 2023; 53 Petro (ref_12) 2014; 4 Lu (ref_15) 2021; 23 Feng (ref_5) 2021; 51
References_xml	– ident: ref_13 doi: 10.1145/2808492.2808511 – volume: 116 start-page: 433 year: 2021 ident: ref_39 article-title: Stochastic Gradient Markov Chain Monte Carlo publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.2020.1847120 – volume: 28 start-page: 4364 year: 2019 ident: ref_25 article-title: Low-Light Image Enhancement via a Deep Hybrid Network publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2019.2910412 – volume: 52 start-page: 3559 year: 2022 ident: ref_7 article-title: N2PN: Non-Reference Two-Pathway Network for Low-Light Image Enhancement publication-title: Appl. Intell. doi: 10.1007/s10489-021-02627-5 – volume: 34 start-page: 397 year: 2020 ident: ref_34 article-title: A Priori Sub-Grid Modelling Using Artificial Neural Networks publication-title: Int. J. Comput. Fluid Dyn. doi: 10.1080/10618562.2020.1789116 – ident: ref_32 doi: 10.1109/CVPR52688.2022.01426 – volume: 6 start-page: 451 year: 1997 ident: ref_9 article-title: Properties and Performance of a Center/Surround Retinex publication-title: IEEE Trans. Image Process. doi: 10.1109/83.557356 – volume: 23 start-page: 4093 year: 2021 ident: ref_15 article-title: TBEFN: A Two-Branch Exposure-Fusion Network for Low-Light Image Enhancement publication-title: IEEE Trans. Multimed. doi: 10.1109/TMM.2020.3037526 – volume: 113 start-page: 35 year: 2021 ident: ref_2 article-title: Recent Advances in Surface Defect Inspection of Industrial Products Using Deep Learning Techniques publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-021-06592-8 – ident: ref_23 – ident: ref_17 doi: 10.1109/FG.2018.00118 – ident: ref_1 doi: 10.3390/app13063861 – ident: ref_21 doi: 10.1109/CVPR52688.2022.00581 – volume: 132 start-page: 208 year: 2023 ident: ref_30 article-title: Context Autoencoder for Self-Supervised Representation Learning publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-023-01852-4 – ident: ref_31 doi: 10.1609/aaai.v37i1.25130 – volume: 516 start-page: 1644 year: 2022 ident: ref_38 article-title: Accelerating Astronomical and Cosmological Inference with Preconditioned Monte Carlo publication-title: Mon. Not. R. Astron. Soc. doi: 10.1093/mnras/stac2272 – ident: ref_11 doi: 10.1109/SITIS.2013.19 – volume: 4 start-page: 71 year: 2014 ident: ref_12 article-title: Multiscale Retinex publication-title: Image Process. Line doi: 10.5201/ipol.2014.107 – ident: ref_28 doi: 10.1145/3343031.3350983 – volume: 53 start-page: 11019 year: 2023 ident: ref_6 article-title: Low-Light Image Enhancement with Geometrical Sparse Representation publication-title: Appl. Intell. doi: 10.1007/s10489-022-04013-1 – ident: ref_29 doi: 10.1109/CVPR52688.2022.01553 – ident: ref_33 – ident: ref_27 – volume: 90 start-page: 103712 year: 2023 ident: ref_18 article-title: R2RNet: Low-Light Image Enhancement via Real-Low to Real-Normal Network publication-title: J. Vis. Commun. Image Represent. doi: 10.1016/j.jvcir.2022.103712 – volume: 31 start-page: 4227 year: 2021 ident: ref_19 article-title: Low-Light Image Enhancement via Progressive-Recursive Network publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2021.3049940 – ident: ref_24 doi: 10.1109/CVPR46437.2021.01042 – ident: ref_26 doi: 10.1145/3394171.3413757 – volume: 17 start-page: 1407 year: 2019 ident: ref_35 article-title: A Priori Estimates of the Population Risk for Two-Layer Neural Networks publication-title: Commun. Math. Sci. doi: 10.4310/CMS.2019.v17.n5.a11 – ident: ref_16 doi: 10.1109/VCIP.2017.8305143 – volume: 241 start-page: 111716 year: 2020 ident: ref_4 article-title: Deep Learning in Environmental Remote Sensing: Achievements and Challenges publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.111716 – volume: 28 start-page: 2190 year: 2018 ident: ref_14 article-title: Single Image Dehazing Based on the Physical Model and MSRCR Algorithm publication-title: IEEE Trans. Circuits Syst. Video Technol. doi: 10.1109/TCSVT.2017.2728822 – volume: 51 start-page: 5111 year: 2021 ident: ref_5 article-title: Low-Light Image Enhancement Based on Multi-Illumination Estimation publication-title: Appl. Intell. doi: 10.1007/s10489-020-02119-y – volume: 30 start-page: 2072 year: 2021 ident: ref_22 article-title: Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2021.3050850 – volume: 6 start-page: 48 year: 2019 ident: ref_3 article-title: Intelligent Video Surveillance: A Review through Deep Learning Techniques for Crowd Analysis publication-title: J. Big Data doi: 10.1186/s40537-019-0212-5 – volume: 61 start-page: 1 year: 1971 ident: ref_8 article-title: Lightness and Retinex Theory publication-title: J. Opt. Soc. Am. doi: 10.1364/JOSA.61.000001 – volume: 6 start-page: 965 year: 1997 ident: ref_10 article-title: A Multiscale Retinex for Bridging the Gap between Color Images and the Human Observation of Scenes publication-title: IEEE Trans. Image Process. doi: 10.1109/83.597272 – ident: ref_20 – volume: 14 start-page: 4607 year: 2021 ident: ref_36 article-title: A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2021.3076198 – volume: 119 start-page: e2109420119 year: 2022 ident: ref_37 article-title: Adaptive Monte Carlo Augmented with Normalizing Flows publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.2109420119
SSID	ssj0000913854
Score	2.2455971
Snippet	The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement...
SourceID	doaj proquest gale crossref
SourceType	Open Website Aggregation Database Index Database
StartPage	190
SubjectTerms	Algorithms Collaboration collaborative constraint dark light enhancement Datasets Deep learning fidelity term Illumination Image enhancement Image restoration Light Methods Modules Noise control Noise reduction Optimization Robustness self-encoding prior Semantics Teaching methods
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS-YwFA0iLtz4GBU_R4csBgaEYtKkr6XfpzKCiqCCu5AnitpK2_H3e29aH4OIG7cli_Tc5N5z2-QcQn6zoGHpplnCHSsSmXqblFnpElFa7irrjJMumk0UZ2fl9XV1_s7qC8-EDfLAA3B7WeBSV4HnhgfJyqr0KHplUs-4CamL3TorqnfNVMzBFRdlJoffmAL6-r3Hm6ZHsdkO6l2KF6j_K0RRr_-zrBxLzdEKWRo5It0f5rZK5nz9gyyPfJGOu7FbI_ZAt3f0BNtrevwAiSE5rG8wivjFj55Gb2g6hTLlaFPT0_HoIL3w9wFG2gbLFj1vb5uWzt6Ww5On6OIZvSP6bp1cHR1ezv4mo2lCYkUh-iSHt9N5SEvLTCYLIwG_3EGRBtBM4QsgPIIDfKHygJLWNuPGcB4M11wGIH8bZL5uar9JKEut9CZjtpC55CY3zOoKDQCND0IwPyG7Lwiqx0EbQ0FPgXCrD3BPyBQxfh2IstbxAQRbjcFWXwV7Qv5ghBRuPoDB6vEOAcwXZazUPv5FhbQk8wnZfgmiGndlp4DaAJ0UkKK2vmM2P8liChRnOMO9Teb79p_fIQv2qb_t2l9xQT4D_fXleg priority: 102 providerName: Directory of Open Access Journals
Title	Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints
URI	https://www.proquest.com/docview/2931053870 https://doaj.org/article/5f14a9f16b1f40898e2612b2e01bf2d0
Volume	11
WOSCitedRecordID	wos001172736100001&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: 2304-6732 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913854 issn: 2304-6732 databaseCode: DOA dateStart: 20140101 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: 2304-6732 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913854 issn: 2304-6732 databaseCode: M~E dateStart: 20140101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2304-6732 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913854 issn: 2304-6732 databaseCode: M7P dateStart: 20140301 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 2304-6732 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913854 issn: 2304-6732 databaseCode: P5Z dateStart: 20140301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2304-6732 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913854 issn: 2304-6732 databaseCode: BENPR dateStart: 20140301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2304-6732 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913854 issn: 2304-6732 databaseCode: PIMPY dateStart: 20140301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELag5cCFlpdYWlY-ICEhWbUT53WqumUrKrGriIdUuFh-thWQLEnokd_OTNbbqkJw4eJDbEUjz9Pj8XyEvORBg-gmGROOF0wm3rIyKx1LSytcZZ1x0o1gE8VyWZ6dVXVMuPWxrHJjE0dD7VqLOfIDcEsQCqQgXoerHwxRo_B2NUJo3CXb2KkM5Hx7Nl_W76-zLNj1sszk-jozhfP9weqiHbDpbA9-L8GH1Lcc0ti3_2_WeXQ5Jzv_S-wueRCDTXq0lo6H5I5vHpGdGHjSqNb9Y2Lf6O4rfYfndHr6HSwMmzcXKA6YOqSLEWSazsDfOdo2dBFrEOkH_y3AStui_6N1d9l29PhGrq48RTjQEYRi6J-QTyfzj8dvWURfYDYt0oHlsD06D0lpuclkYWQAo-rA24vEmMIXEDmlwiQ-VB62WWubCWOECEZoIQNEkU_JVtM2_hmhPLHSm4zbQuZSmNxwqytEEjQ-pCn3E_J6wwK1WjfZUHA4QX6pP_g1ITNk0vVC7I89fmi7cxXVTWVArK6CyI0IkpdV6bFVGlDLhQmJg5-8QhYr1GLYBqvjYwSgF_thqSO8jgX7JvMJ2d-wWEX17tUNf5__e3qP3E8gClqXee-TraH76V-Qe_ZquOy7aZTW6ZgImGLZaY3jrzmMdfYF5uvTRf35N7eA-vw
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFLWqKRJsaHmpA6V4AUJCimo7zmuBqj7VUWdGI1GksjLxi1ZAMiShiJ_qN3JvHq0Qgl0XbBMrcuLjc26u7XsIecl8DtAVUcAtSwIpnAnSKLVBmBpuM2O1lbY1m0jm8_TsLFuskKvhLAxuqxw4sSVqWxrMkW-DLEEoEAK8dpbfAnSNwtXVwUKjg8WJ-_kDftnqt5MDGN9XQhwdnu4fB72rQGDCJGyCGAKkPPYiNUxHMtHSA1lYUDEutE5cAhFByLVwPnMQf-S5ibjWnHvNcy59hAlQoPxVCWBnI7K6mMwWH66zOlhlM41kt3wahhnbXp6XDRa5rUFnBR7c_k0AW5-Av6lBK3FHa__bx1kn9_tgmu526H9AVlzxkKz1gTXtaat-RMxBXn2mU8xD0MlXYNDgsDhHuGNqlM5aE226B3puaVnQWb_Hkr5zXzy0NCXqO11UF2VF92_mzaWjaHfammw09WPy_lZe9QkZFWXhNghlwkinI2YSGUuuY81MnqFTonY-DJkbkzfDkKtlV0REwc8X4kP9gY8x2UNQXDfE-t_thbL6pHo6URF0Ns88jzX3kqVZ6rAUHPSWce2FhYe8RkgpZCn4DCbvD1tAf7Hel9rF5WbgbxmPyeYAKdXTV61u8PT037dfkLvHp7Opmk7mJ8_IPQERX7elfZOMmuq7e07umMvmoq62-plCycfbxt8vtNNQ5g
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELaqLUJcKE91oYAPICSkaP3K64BQt7srVm1XKx5SbyF-0QpIliQU8df4dczk0QohuPXANbEix_7mm_HYno-Qp8znAF0RBtyyOFDCmSAJExvIxHCbGqutsq3YRLxaJScn6XqL_BzuwuCxyoETW6K2pcEc-QTcEoQCEuA18f2xiPVs8WrzNUAFKdxpHeQ0Oogcuh_fYflWv1zOYK6fCbGYvzt4HfQKA4GRsWyCCIKlPPIiMUyHKtbKA3FY8GhcaB27GKIDybVwPnUQi-S5CbnWnHvNc658iMlQoP_tWMKiZ0S2p_PV-s1Fhgcrbiah6rZSpUzZZHNaNljwtgafK_AS92_OsNUM-JtnaN3dYud_Hqhb5GYfZNP9zipuky1X3CE7fcBNezqr7xIzy6tP9AjzE3T5BZg1mBenaAaYMqXHrbg2nYKft7Qs6HF_9pK-dZ89tDQl-n26rs7Kih5c2tO5oyiD2opvNPU98v5KfvU-GRVl4XYJZcIop0NmYhUpriPNTJ6igqJ2XkrmxuTFMP3ZpisuksGiDLGS_YGVMZkiQC4aYl3w9kFZfcx6mslC6Gyeeh5p7hVL0sRhiTjoLePaCwsfeY7wypC9YBhM3l_CgP5iHbBsH7ehgddVNCZ7A7yyntbq7BJbD_79-gm5DqDLjparw4fkhoBAsDvpvkdGTfXNPSLXzHlzVlePe6Oh5MNVw-8XWQlZgA
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=Dark+Light+Image-Enhancement+Method+Based+on+Multiple+Self-Encoding+Prior+Collaborative+Constraints&rft.jtitle=Photonics&rft.au=Guan%2C+Lei&rft.au=Dong%2C+Jiawei&rft.au=Li%2C+Qianxi&rft.au=Huang%2C+Jijiang&rft.date=2024-02-01&rft.issn=2304-6732&rft.eissn=2304-6732&rft.volume=11&rft.issue=2&rft.spage=190&rft_id=info:doi/10.3390%2Fphotonics11020190&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_photonics11020190
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2304-6732&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2304-6732&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2304-6732&client=summon