Publication:
Lighting enhancement of underwater image using coronavirus herd immunity optimizer

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dc.citedby1
dc.contributor.authorAlyasseri Z.A.A.en_US
dc.contributor.authorGhalib R.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorMohammed H.J.en_US
dc.contributor.authorAli N.en_US
dc.contributor.authorAli N.S.en_US
dc.contributor.authorAl-Wesabi F.N.en_US
dc.contributor.authorAssiri M.en_US
dc.contributor.authorid57862594800en_US
dc.contributor.authorid58654292500en_US
dc.contributor.authorid36682671900en_US
dc.contributor.authorid57202657688en_US
dc.contributor.authorid54985243500en_US
dc.contributor.authorid56693765600en_US
dc.contributor.authorid57211901842en_US
dc.contributor.authorid57219344932en_US
dc.date.accessioned2025-03-03T07:44:26Z
dc.date.available2025-03-03T07:44:26Z
dc.date.issued2024
dc.description.abstractRecently, the technology of Underwater computer vision has played a vital role by improving the quality of underwater images owing to its significance in different applications in marines, such as military, resource development, biological research, and underwater environmental assessments. Moreover, light is absorbed and scattered while propagating through water, leading to color distortion. Additionally, floating micro-particles in the water contribute to low image contrast, resulting in blurry and poorly lit underwater images with a color cast. Therefore, many researchers have been attracted to developing diverse computer vision-based methods to improve the quality of underwater images, such as restoration, enhancement, and deep-learning techniques to restore and enhance degraded underwater images. Although numerous studies have attempted to address these issues, there is still much room for improvement in the quality of the produced images. To this end, this paper proposes a new enhancement method to improve underwater image quality. The presented approach utilizes the Coronavirus herd immunity optimizer algorithm for underwater image enhancement (CHIO-UIE) and is evaluated using standard measures on public datasets. The empirical results demonstrate that the CHIO-UIE method enhances the quality of images based on qualitative and quantitative evaluations, successfully improving underwater images with low contrast and light by significantly enhancing the visual impact of distorted underwater images across various underwater environments. ? 2024 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2024.01.009
dc.identifier.epage125
dc.identifier.scopus2-s2.0-85185507865
dc.identifier.spage115
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185507865&doi=10.1016%2fj.aej.2024.01.009&partnerID=40&md5=c628267325b5a6b27c15e86591a8322e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36755
dc.identifier.volume91
dc.pagecount10
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.subjectComputer vision
dc.subjectDeep learning
dc.subjectEnergy efficiency
dc.subjectEnvironmental technology
dc.subjectImage enhancement
dc.subjectImage reconstruction
dc.subjectMilitary applications
dc.subjectMilitary photography
dc.subjectQuality control
dc.subjectRestoration
dc.subjectBiological research
dc.subjectCHIO
dc.subjectCoronaviruses
dc.subjectHerd immunities
dc.subjectMetahurstic algorithm
dc.subjectMilitary resources
dc.subjectOptimizers
dc.subjectResource development
dc.subjectUnderwater computer vision
dc.subjectUnderwater image enhancements
dc.subjectCoronavirus
dc.titleLighting enhancement of underwater image using coronavirus herd immunity optimizeren_US
dc.typeArticleen_US
dspace.entity.typePublication
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