Lightening Network for Low-light Image Enhancement

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-...

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Bibliographic Details
Published in:IEEE transactions on image processing 2020-01, Vol.29, p.1-1
Main Authors: Wang, Li-Wen, Liu, Zhi-Song, Siu, Wan-Chi, Lun, Daniel Pak-Kong
Format: Article
Language:English
Subjects:
Artificial neural networks
Darkening
Data mining
deep learning
Feature extraction
Image enhancement
image processing
Image resolution
Light
Lighting
low-light image enhancement
Pictures
Reflectivity
Task analysis
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Summary: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.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2020.3008396