Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 문영식 | - |
dc.date.accessioned | 2023-08-21T06:46:11Z | - |
dc.date.available | 2023-08-21T06:46:11Z | - |
dc.date.issued | 2016-06 | - |
dc.identifier.citation | 2016년 대한전자공학회 하계학술대회 논문집, Page. 978-981 | - |
dc.identifier.uri | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE06724584 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/185535 | - |
dc.description.abstract | This paper proposes a method for recovering the intrinsic details of an image that cannot be reconstructed by interpolation, named as residual images, through a convolutional neural network with a deconvolutional layer. The predicted residual image is added to an interpolated LR image to reconstruct the lost details. In both the qualitative and quantitative comparison to SRCNN, the proposed framework performed in a better manner. The proposed framework did not produce the false edges seen in the results of SRCNN. Furthermore, the proposed method resulted 0.18 dB higher PSNR in average, compared to SRCNN. | - |
dc.description.sponsorship | This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government. (MEST) (NO. 2012002464) | - |
dc.language | en | - |
dc.publisher | 대한전자공학회 | - |
dc.title | Single Image Super-Resolution using Residual Image based on CNN with a Deconvolutional Layer | - |
dc.title.alternative | 디컨볼루셔널 레이어를 포함하는 CNN 기반 레지듀얼 이미지 이용 단일 영상 초해상도 복원 | - |
dc.type | Article | - |
dc.relation.page | 978-981 | - |
dc.relation.journal | 2016년 대한전자공학회 하계학술대회 논문집 | - |
dc.contributor.googleauthor | Kang Ho Shin | - |
dc.contributor.googleauthor | Woo Jin Jeong | - |
dc.contributor.googleauthor | Young Shik Moon | - |
dc.sector.campus | E | - |
dc.sector.daehak | 소프트웨어융합대학 | - |
dc.sector.department | 소프트웨어학부 | - |
dc.identifier.pid | ysmoon | - |
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