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dc.contributor.authorChiu, Mian Jhongen_US
dc.contributor.authorWang, Guo-Zhenen_US
dc.contributor.authorChuang, Jen-Huien_US
dc.date.accessioned2019-10-05T00:09:46Z-
dc.date.available2019-10-05T00:09:46Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-7281-0397-6en_US
dc.identifier.issn0271-4302en_US
dc.identifier.urihttp://hdl.handle.net/11536/152952-
dc.description.abstractWhile 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.isoen_USen_US
dc.subjectlight-weight convolutional networken_US
dc.subjectlow-light imagingen_US
dc.titleFast Imaging in the Dark by using Convolutional Networken_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS)en_US
dc.citation.spage0en_US
dc.citation.epage0en_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.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000483076400141en_US
dc.citation.woscount0en_US
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