DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Bomi | ko |
dc.contributor.author | Lee, Sunhyeok | ko |
dc.contributor.author | Kim, Nahyun | ko |
dc.contributor.author | Jang, Donggon | ko |
dc.contributor.author | Kim, Dae-Shik | ko |
dc.date.accessioned | 2022-05-20T06:00:51Z | - |
dc.date.available | 2022-05-20T06:00:51Z | - |
dc.date.created | 2021-11-17 | - |
dc.date.created | 2021-11-17 | - |
dc.date.issued | 2022-01-05 | - |
dc.identifier.citation | 22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, pp.904 - 912 | - |
dc.identifier.issn | 2472-6737 | - |
dc.identifier.uri | http://hdl.handle.net/10203/296629 | - |
dc.description.abstract | Color conveys important information about the visible world. However, under low-light conditions, both pixel intensity, as well as true color distribution, can be significantly shifted. Moreover, most of such distortions are non-recoverable due to inverse problems. In the present study, we utilized recent advancements in learning-based methods for low-light image enhancement. However, while most "deep learning"methods aim to restore high-level and object-oriented visual information, we hypothesized that learning-based methods can also be used for restoring color-based information. To address this question, we propose a novel color representation learning method for low-light image enhancement. More specifically, we used a channel-aware residual network and a differentiable intensity histogram to capture color features. Experimental results using synthetic and natural datasets suggest that the proposed learning scheme achieves state-of-the-art performance. We conclude from our study that inter-channel dependency and color distribution matching are crucial factors for learning color representations under low-light conditions. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Learning Color Representations for Low-Light Image Enhancement | - |
dc.type | Conference | - |
dc.identifier.wosid | 000800471200091 | - |
dc.identifier.scopusid | 2-s2.0-85126118359 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 904 | - |
dc.citation.endingpage | 912 | - |
dc.citation.publicationname | 22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Waikoloa | - |
dc.identifier.doi | 10.1109/WACV51458.2022.00098 | - |
dc.contributor.localauthor | Kim, Dae-Shik | - |
dc.contributor.nonIdAuthor | Kim, Bomi | - |
dc.contributor.nonIdAuthor | Kim, Nahyun | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.