Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 문영식 | - |
dc.date.accessioned | 2020-01-13T05:13:34Z | - |
dc.date.available | 2020-01-13T05:13:34Z | - |
dc.date.issued | 2019-02 | - |
dc.identifier.citation | Advances in Science, Technology and Engineering Systems, v. 4, No. 1, Page. 159-164 | en_US |
dc.identifier.issn | 2415-6698 | - |
dc.identifier.uri | https://astesj.com/v04/i01/p15/ | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/121721 | - |
dc.description.abstract | Low contrast images degrade the performance of image processing system. To solve the issue, plenty of image enhancement methods have been proposed. But the methods work properly on the fixed environment or specific images. The methods dependent on fixed image conditions cannot perform image enhancement properly and perspective of smart device users, algorithms including iterative calculations are inconvenient for users. To avoid these issues, we propose a locally adaptive contrast enhancement method using CNN and simple reflection model. The experimental results show that the proposed method reduces over-enhancement, while recovering the details of the low contrast regions. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | ASTES Publishers | en_US |
dc.subject | Image Enhancement | en_US |
dc.subject | Convolutional Neural Network | en_US |
dc.subject | Reflection Model | en_US |
dc.subject | Machine Learning | en_US |
dc.title | Low contrast image enhancement using convolutional neural network with simple reflection model | en_US |
dc.type | Article | en_US |
dc.relation.no | 1 | - |
dc.relation.volume | 4 | - |
dc.identifier.doi | 10.25046/aj040115 | - |
dc.relation.page | 159-164 | - |
dc.relation.journal | Advances in Science, Technology and Engineering Systems | - |
dc.contributor.googleauthor | Moon, Young Shik | - |
dc.contributor.googleauthor | Han, Bok Gyu | - |
dc.contributor.googleauthor | Yang, Hyeon Seok | - |
dc.contributor.googleauthor | Lee, Ho Gyeong | - |
dc.relation.code | 2020000716 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | DIVISION OF COMPUTER SCIENCE | - |
dc.identifier.pid | ysmoon | - |
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