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
Journal
IEEE Transactions on Image Processing
Volume
29
Start page
7984
End page
7996
Abstract
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.
SFU Affiliated Publication
No
Availability at SFU Library
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