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Please use this identifier to cite or link to this item: https://digital.lib.ueh.edu.vn/handle/UEH/62244
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dc.contributor.authorThanh L.T.-
dc.contributor.otherThanh D.N.H.-
dc.contributor.otherHien N.N.-
dc.contributor.otherErkan U.-
dc.contributor.otherPrasath V.B.S.-
dc.date.accessioned2021-09-05T02:41:43Z-
dc.date.available2021-09-05T02:41:43Z-
dc.date.issued2021-
dc.identifier.urihttp://digital.lib.ueh.edu.vn/handle/UEH/62244-
dc.description.abstractImage dehazing is an important problem and it is useful as a preprocessing step in various automatic image analysis systems. The goal of image dehazing is the quality improvement of digital images by removing haze across the scene. In the present work, we consider an automatic image dehazing approach that is based on optimal color channels and nonlinear transformations. The proposed dehazing approach can remove haze fast and effectively with features preservation. In our experiments, we compare the image dehazing results with related image dehazing methods from the literature. Visual assessments, as well as quantitative assessments, are also done to show the improvements obtained by the dehazing model across different natural images. Obtained experimental results indicate that the dehazing approach proposed here performs better than other dehazing models in terms of overall better visual quality and higher blind image quality metric values.en
dc.formatPortable Document Format (PDF)-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.relation.ispartof2020 IEEE Eighth International Conference on Communications and Electronics (ICCE)-
dc.rightsIEEE-
dc.subjectDefoggingen
dc.subjectDehazingen
dc.subjectGamma correctionen
dc.subjectHistogram equalizationen
dc.subjectHSVen
dc.subjectImage processingen
dc.subjectImage restorationen
dc.titleSingle image dehazing with optimal color channels and nonlinear transformationen
dc.typeConference Paperen
dc.identifier.doihttps://doi.org/10.1109/ICCE48956.2021.9352087-
dc.format.firstpage421-
dc.format.lastpage426-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextOnly abstracts-
item.openairetypeConference Paper-
item.languageiso639-1en-
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