完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Chiu, Mian Jhong | en_US |
dc.contributor.author | Wang, Guo-Zhen | en_US |
dc.contributor.author | Chuang, Jen-Hui | en_US |
dc.date.accessioned | 2019-10-05T00:09:46Z | - |
dc.date.available | 2019-10-05T00:09:46Z | - |
dc.date.issued | 2019-01-01 | en_US |
dc.identifier.isbn | 978-1-7281-0397-6 | en_US |
dc.identifier.issn | 0271-4302 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/152952 | - |
dc.description.abstract | While fast imaging in low-light condition is crucial for surveillance and robot applications, it is still a formidable challenge to resolve the seemingly inevitable high noise level and low photon count issues. A variety of image enhancement methods such as de-blurring and de-noising have been proposed in the past. However, limitations can still be found in these methods under extreme low-light condition. To overcome such difficulty, a learning-based image enhancement approach is proposed in this paper. In order to support the development of learning-based methodology, we collected a new low-lighting dataset (<0.1 lux) of raw short-exposure (6.67 ms) images, as well as the corresponding long-exposure reference images. Based on such dataset, we develop a light-weight convolutional network structure which is involved with fewer parameters and has lower computation cost compared with a regular-size network. The presented work is expected to make possible the implementation of more advanced edge devices, and their applications. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | light-weight convolutional network | en_US |
dc.subject | low-light imaging | en_US |
dc.title | Fast Imaging in the Dark by using Convolutional Network | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | 2019 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) | en_US |
dc.citation.spage | 0 | en_US |
dc.citation.epage | 0 | en_US |
dc.contributor.department | 交大名義發表 | zh_TW |
dc.contributor.department | 資訊工程學系 | zh_TW |
dc.contributor.department | National Chiao Tung University | en_US |
dc.contributor.department | Department of Computer Science | en_US |
dc.identifier.wosnumber | WOS:000483076400141 | en_US |
dc.citation.woscount | 0 | en_US |
顯示於類別: | 會議論文 |