Deep lightening network for low-light image enhancement

Author(s)
Liu, Zhisong
Author(s)
Wang, L.-W.
Lun, D. P.-K.
Date Issued
2020
Publisher
IEEE
Related Publication(s)
Proceedings of the 2020 IEEE International Symposium on Circuits and Systems (ISCAS)
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.
URI
DOI
SFU Affiliated Publication
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