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Title: Lightening network for low-light image enhancement
Author(s): Siu, Wan Chi 
Liu, Zhisong 
Author(s): Wang, L.-W.
Lun, D. P. K.
Issue Date: 2020
Publisher: IEEE
Journal: IEEE Transactions on Image Processing 
Volume: 29
Start page: 7984
End page: 7996
Low-light image enhancement is a challenging task that has attracted considerable attention. Pictures taken in low-light conditions often have bad visual quality. To address the problem, we regard the low-light enhancement as a residual learning problem that is to estimate the residual between low- and normal-light images. In this paper, we propose a novel Deep Lightening Network (DLN) that benefits from the recent development of Convolutional Neural Networks (CNNs). The proposed DLN consists of several Lightening Back-Projection (LBP) blocks. The LBPs perform lightening and darkening processes iteratively to learn the residual for normal-light estimations. To effectively utilize the local and global features, we also propose a Feature Aggregation (FA) block that adaptively fuses the results of different LBPs. We evaluate the proposed method on different datasets. Numerical results show that our proposed DLN approach outperforms other methods under both objective and subjective metrics.
DOI: 10.1109/TIP.2020.3008396
CIHE Affiliated Publication: No
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