ReX-Net: A reflectance-guided underwater image enhancement network for extreme scenarios

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dc.contributor.authorZhang, Dehuan
dc.contributor.authorZhou, Jingchun
dc.contributor.authorZhang, Weishi
dc.contributor.authorLin, Zifan
dc.contributor.authorYao, Jian
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-09-06T12:18:12Z
dc.date.available2023-09-06T12:18:12Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis work was supported in part by the National Natural Science Foundation of China (No. 61702074) , the Liaoning Provincial Natural Science Foundation of China (No. 20170520196) , the Fundamental Research Funds for the Central Universities, China (Nos. 3132019205 and 3132019354) , and the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University.en_US
dc.description.abstractDue to the complex underwater environment, underwater images exhibit different degradation characteristics, severely affecting their practical applications. Although underwater image enhancement networks with physical priors exist, the statistical priors are not applicable in extreme underwater scenes. Therefore, we propose ReXNet, a reflectance-guided underwater image enhancement network for extreme scenarios. ReX-Net leverages the complementary information of reflectance and the underwater image obtained through the original encoder and reflectance encoder to minimize the impact of different scene environments. As underwater images contain object information at different scales, the encoder includes a TriFuse reflected-image object extractor module (TRIOE), which employs Tri-scale convolutions to capture features at different scales and utilize attention mechanisms to enhance channel and spatial information. In the decoder, we design a context sensitive multi-level integration module (CSMLI) to fuse feature vectors at different resolutions, thereby improving the expressiveness and robustness of features while avoiding artifacts and ensuring pixel accuracy. Experiments on multiple datasets demonstrate that ReX-Net outperforms existing methods. Furthermore, application experiments show the practicality of ReX-Net in other visualization tasks.en_US
dc.description.sponsorshipNational Natural Science Foundation of China [61702074]; Liaoning Provincial Natural Science Foundation of China [20170520196]; Fundamental Research Funds for the Central Universities, China [3132019205, 3132019354]; Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime Universityen_US
dc.identifier.citationZhang, D., Zhou, J., Zhang, W., Lin, Z., Yao, J., Polat, K., ... & Alhudhaif, A. (2023). ReX-Net: A reflectance-guided underwater image enhancement network for extreme scenarios. Expert Systems with Applications, 120842.en_US
dc.identifier.doi10.1016/j.eswa.2023.120842
dc.identifier.endpage14en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85162118407en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2023.120842
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11668
dc.identifier.volume231en_US
dc.identifier.wosWOS:001023069700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectUnderwater Image Enhancementen_US
dc.subjectRetinex Theoryen_US
dc.subjectUNeten_US
dc.subjectImage Prioren_US
dc.subjectQuality Assessmenten_US
dc.subjectVisibilityen_US
dc.titleReX-Net: A reflectance-guided underwater image enhancement network for extreme scenariosen_US
dc.typeArticleen_US

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