Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19162
DC FieldValueLanguage
dc.contributor.authorMangeruga, Marino-
dc.contributor.authorCozza, Marco-
dc.contributor.authorBruno, Fabio-
dc.date.accessioned2020-10-15T10:37:53Z-
dc.date.available2020-10-15T10:37:53Z-
dc.date.issued2018-01-16-
dc.identifier.citationJournal of Marine Science and Engineering, 2018 Vol. 6, no. 1en_US
dc.identifier.issn20771312-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/19162-
dc.description.abstractUnderwater images usually suffer from poor visibility, lack of contrast and colour casting, mainly due to light absorption and scattering. In literature, there are many algorithms aimed to enhance the quality of underwater images through different approaches. Our purpose was to identify an algorithm that performs well in different environmental conditions. We have selected some algorithms from the state of the art and we have employed them to enhance a dataset of images produced in various underwater sites, representing different environmental and illumination conditions. These enhanced images have been evaluated through some quantitative metrics. By analysing the results of these metrics, we tried to understand which of the selected algorithms performed better than the others. Another purpose of our research was to establish if a quantitative metric was enough to judge the behaviour of an underwater image enhancement algorithm. We aim to demonstrate that, even if the metrics can provide an indicative estimation of image quality, they could lead to inconsistent or erroneous evaluationsen_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relationAdvanced VR, iMmersive serious games and Augmented REality as tools to raise awareness and access to European underwater CULTURal heritageen_US
dc.relation.ispartofJournal of Marine Science and Engineeringen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectUnderwater image enhancementen_US
dc.subjectDehazingen_US
dc.subjectColour correctionen_US
dc.subjectAutomatic colour equalizationen_US
dc.subjectContrast Limiteden_US
dc.subjectAdaptive Histogram Equalization (CLAHE)en_US
dc.titleEvaluation of Underwater Image Enhancement Algorithms under Different Environmental Conditionsen_US
dc.typeArticleen_US
dc.collaboration3D Research s.r.l.en_US
dc.collaborationUniversity of Calabriaen_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryItalyen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.3390/jmse6010010en_US
dc.relation.issue1en_US
dc.relation.volume6en_US
cut.common.academicyear2018-2019en_US
item.openairetypearticle-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.languageiso639-1en-
item.fulltextWith Fulltext-
crisitem.project.grantnoH2020 RIA CULT-COOP-08-2016-
crisitem.project.fundingProgramH2020-
crisitem.project.openAireinfo:eu-repo/grantAgreement/EC/H2020/727153-
crisitem.journal.journalissn2077-1312-
crisitem.journal.publisherMDPI-
Appears in Collections:Άρθρα/Articles
Files in This Item:
File Description SizeFormat
Evaluation of Underwater.pdf20.58 MBAdobe PDFView/Open
CORE Recommender
Show simple item record

SCOPUSTM   
Citations

40
checked on Nov 6, 2023

WEB OF SCIENCETM
Citations

32
Last Week
0
Last month
0
checked on Oct 29, 2023

Page view(s) 50

375
Last Week
2
Last month
6
checked on Dec 3, 2024

Download(s)

151
checked on Dec 3, 2024

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons