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Comprehensive Review of Machine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniques

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dc.citedby7
dc.contributor.authorArif Z.H.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorAbdulkareem K.H.en_US
dc.contributor.authorMohammed M.A.en_US
dc.contributor.authorAl-Mhiqani M.N.en_US
dc.contributor.authorMutlag A.A.en_US
dc.contributor.authorDama�evi?ius R.en_US
dc.contributor.authorid57350531200en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid57197854295en_US
dc.contributor.authorid57192089894en_US
dc.contributor.authorid57197853907en_US
dc.contributor.authorid57203180481en_US
dc.contributor.authorid6603451290en_US
dc.date.accessioned2023-05-29T09:38:25Z
dc.date.available2023-05-29T09:38:25Z
dc.date.issued2022
dc.descriptionExtraction; Feature extraction; Image denoising; Image segmentation; Classification technique; Feature extraction and classification; Feature extraction techniques; Features extraction; Images processing; Machine learning algorithms; Machine learning methods; Research areas; Sensory system; Visual sensory; Image classificationen_US
dc.description.abstractImages captured through a visual sensory system are degraded in a foggy scene, which negatively influences recognition, tracking, and detection of targets. Efficient tools are needed to detect, pre-process, and enhance foggy scenes. Machine learning (ML) has a significant role in image defogging domain for tackling adverse issues. Unfortunately, regardless of contributions that were made by ML, little attention has been attributed to this topic. This paper summarizes the role of ML methods and relevant aspects in the image defogging research area. Also, the basic terms and concepts are highlighted in image defogging topic. Feature extraction approaches with a summary of advantages and disadvantages are described. ML algorithms are also summarized that have been used for applications related to image defogging, that is, image denoising, image quality assessment, image segmentation, and foggy image classification. Open datasets are also discussed. Finally, the existing problems of the image defogging domain in general and, specifically related to ML which need to be further studied are discussed. To the best knowledge, this the first review paper which sheds a light on the role of ML and relevant aspects in the image defogging domain. � 2021 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technologyen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1049/ipr2.12365
dc.identifier.epage310
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85119670133
dc.identifier.spage289
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85119670133&doi=10.1049%2fipr2.12365&partnerID=40&md5=9197f42d86a00006521d4c3e44aa593a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26987
dc.identifier.volume16
dc.publisherJohn Wiley and Sons Incen_US
dc.sourceScopus
dc.sourcetitleIET Image Processing
dc.titleComprehensive Review of Machine Learning (ML) in Image Defogging: Taxonomy of Concepts, Scenes, Feature Extraction, and Classification techniquesen_US
dc.typeReviewen_US
dspace.entity.typePublication
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