Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/1251
DC FieldValueLanguage
dc.contributor.authorSiu, Wan Chien_US
dc.contributor.authorLiu, Zhisong-
dc.contributor.otherWang, L.-W.-
dc.contributor.otherLun, D. P.-K.-
dc.date.accessioned2021-08-11T09:03:12Z-
dc.date.available2021-08-11T09:03:12Z-
dc.date.issued2020-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/1251-
dc.description.abstractWe propose a Deep Lightening Network (DLN) for low-light image enhancement. Inspire by the domain transfer study, we propose a novel cycle learning structure to learn the mapping relationship between low- and normal-light images. Each DLN consists of several Lightening Back-Projection (LBP) blocks that learn the residual between low- and normal-light images. To efficiently estimate the local and global information, we fuse the features from different LBP results. Experimental results on different datasets show that our proposed DLN approach outperforms other approaches in all objective and subjective measures.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.titleDeep lightening network for low-light image enhancementen_US
dc.typeconference proceedingsen_US
dc.relation.publicationProceedings of the 2020 IEEE International Symposium on Circuits and Systems (ISCAS)en_US
dc.identifier.doi10.1109/ISCAS45731.2020.9180751-
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.relation.isbn9781728133201en_US
dc.cihe.affiliatedNo-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.cerifentitytypePublications-
item.openairetypeconference proceedings-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
crisitem.author.deptSchool of Computing and Information Sciences-
crisitem.author.deptSchool of Computing and Information Sciences-
crisitem.author.orcid0000-0001-8280-0367-
crisitem.author.orcid0000-0003-4507-3097-
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