@inproceedings{ff57fd185e28464c815df34bbc11ca46,
title = "Deep lightening network for low-light image enhancement",
abstract = "We propose a Deep Lightening Network (DLN) for low-light image enhancement. Inspire by the domain transfer study, we propose a novel cycle learning structure to learn the mapping relationship between low- and normal-light images. Each DLN consists of several Lightening Back-Projection (LBP) blocks that learn the residual between low- and normal-light images. To efficiently estimate the local and global information, we fuse the features from different LBP results. Experimental results on different datasets show that our proposed DLN approach outperforms other approaches in all objective and subjective measures.",
keywords = "Deep learning, Image processing, Low-light image enhancement",
author = "Wang, {Li Wen} and Liu, {Zhi Song} and Siu, {Wan Chi} and {Pak-Kong Lun}, Daniel",
note = "Funding Information: This work was supported in part with a PhD research studentship to Li-Wen Wang by The Hong Kong Polytechnic University. Publisher Copyright: {\textcopyright} 2020 IEEE; 52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020 ; Conference date: 10-10-2020 Through 21-10-2020",
year = "2020",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Proceedings",
}