Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정제창 | - |
dc.date.accessioned | 2020-06-04T07:33:49Z | - |
dc.date.available | 2020-06-04T07:33:49Z | - |
dc.date.issued | 2019-06 | - |
dc.identifier.citation | 한국방송·미디어공학회 2019 하계학술대회, Page. 102-105 | en_US |
dc.identifier.uri | http://www.dbpia.co.kr/view/ar_view.asp?arid=4801357 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/151456 | - |
dc.description.abstract | In the classical single-image super-resolution (SISR) reconstruction method using convolutional neural networks, the extracted features are not fully utilized, and the training time is too long. Aiming at the above problems, we proposed an improved SISR method based on a residual network. Our proposed method uses a feature fusion technology based on improved residual blocks. The advantage of this method is the ability to fully and effectively utilize the features extracted from the shallow layers. In addition, we can see that the feature fusion can adaptively preserve the information from current and previous residual blocks and stabilize the training for deeper network. And we use the global residual learning to make network training easier. The experimental results show that the proposed method gets better performance than classic reconstruction methods. | en_US |
dc.description.sponsorship | This work was supported by the Brain Korea 21 plus Project in 2014. | en_US |
dc.language.iso | en | en_US |
dc.publisher | 한국방송∙미디어공학회 | en_US |
dc.title | Improved Residual Network for Single Image Super Resolution | en_US |
dc.type | Article | en_US |
dc.relation.page | 1-4 | - |
dc.contributor.googleauthor | Xu, Yinxiang | - |
dc.contributor.googleauthor | Wee, Seungwoo | - |
dc.contributor.googleauthor | Jeong, Jechang | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF ELECTRONIC ENGINEERING | - |
dc.identifier.pid | jjeong | - |
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