Spatially High Resolution Visible and Near-Infrared Separation using Conditional Generative Adversarial Network and Color Brightness Transfer Method
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Y.[Park, Y.] | - |
dc.contributor.author | Jeon, B.[Jeon, B.] | - |
dc.date.accessioned | 2021-07-29T21:46:03Z | - |
dc.date.available | 2021-07-29T21:46:03Z | - |
dc.date.created | 2019-11-29 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/23530 | - |
dc.description.abstract | Since near-infrared (NIR) image information is useful in improving visible range (VIS) image, acquisition of both images in a more simple and economic way has drawn much research interest. Deep-learning based approach is found to be effective in the separation from a mixed NIR and VIS image captured by a conventional camera, however, it has a problem of high computational complexity, especially for an image of high spatial resolution. In this paper, we propose a method for separating high-resolution VIS and NIR images using a deep-learning based on a conditional generative adversarial network. Experimental results show that the proposed method can reduce the computational complexity by 97 times as compared with the previous work without loss in image quality. © 2018 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.subject | Complex networks | - |
dc.subject | Computational complexity | - |
dc.subject | Deep learning | - |
dc.subject | Image acquisition | - |
dc.subject | Image enhancement | - |
dc.subject | Separation | - |
dc.subject | Adversarial networks | - |
dc.subject | Conventional camera | - |
dc.subject | High resolution visible | - |
dc.subject | High spatial resolution | - |
dc.subject | Image information | - |
dc.subject | Learning-based approach | - |
dc.subject | Near Infrared | - |
dc.subject | Research interests | - |
dc.subject | Infrared devices | - |
dc.title | Spatially High Resolution Visible and Near-Infrared Separation using Conditional Generative Adversarial Network and Color Brightness Transfer Method | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Y.[Park, Y.] | - |
dc.contributor.affiliatedAuthor | Jeon, B.[Jeon, B.] | - |
dc.identifier.doi | 10.1109/ICCCAS.2018.8769279 | - |
dc.identifier.scopusid | 2-s2.0-85070353530 | - |
dc.identifier.bibliographicCitation | 10th International Conference on Communications, Circuits and Systems, ICCCAS 2018, pp.426 - 430 | - |
dc.relation.isPartOf | 10th International Conference on Communications, Circuits and Systems, ICCCAS 2018 | - |
dc.citation.title | 10th International Conference on Communications, Circuits and Systems, ICCCAS 2018 | - |
dc.citation.startPage | 426 | - |
dc.citation.endPage | 430 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 3 | - |
dc.subject.keywordPlus | Complex networks | - |
dc.subject.keywordPlus | Computational complexity | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Image acquisition | - |
dc.subject.keywordPlus | Image enhancement | - |
dc.subject.keywordPlus | Separation | - |
dc.subject.keywordPlus | Adversarial networks | - |
dc.subject.keywordPlus | Conventional camera | - |
dc.subject.keywordPlus | High resolution visible | - |
dc.subject.keywordPlus | High spatial resolution | - |
dc.subject.keywordPlus | Image information | - |
dc.subject.keywordPlus | Learning-based approach | - |
dc.subject.keywordPlus | Near Infrared | - |
dc.subject.keywordPlus | Research interests | - |
dc.subject.keywordPlus | Infrared devices | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | GAN | - |
dc.subject.keywordAuthor | near-infrared | - |
dc.subject.keywordAuthor | separation | - |
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