Low-light Image Enhancement Using Dual Convolutional Neural Networks for Vehicular Imaging Systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ha, Eunjae | - |
dc.contributor.author | Lim, Heunseung | - |
dc.contributor.author | Yu, Soohwan | - |
dc.contributor.author | Paik, Joonki | - |
dc.date.accessioned | 2021-05-20T07:40:48Z | - |
dc.date.available | 2021-05-20T07:40:48Z | - |
dc.date.issued | 2020-01 | - |
dc.identifier.issn | 0747-668X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44023 | - |
dc.description.abstract | This paper presents a low-light image enhancement method using a convolutional neural network (CNN). Given a low-light input image, the proposed method converts RGB color space to CIELAB color space. The luminance and chrominance components are separately enhanced. The luminance channel is enhanced using a CNN to enhance the brightness. On the other hand, the chrominance channels are enhanced using a dilated CNN to reduce the color distortion. Experimental results demonstrate that the proposed method can successfully enhance low-light images of a vehicular imaging system without color distortion. | - |
dc.format.extent | 2 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE | - |
dc.title | Low-light Image Enhancement Using Dual Convolutional Neural Networks for Vehicular Imaging Systems | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICCE46568.2020.9043035 | - |
dc.identifier.bibliographicCitation | 2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), v.2020-Janua, pp 739 - 740 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000612997400181 | - |
dc.identifier.scopusid | 2-s2.0-85082583455 | - |
dc.citation.endPage | 740 | - |
dc.citation.startPage | 739 | - |
dc.citation.title | 2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE) | - |
dc.citation.volume | 2020-Janua | - |
dc.type.docType | Proceedings Paper | - |
dc.publisher.location | 미국 | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.