Lightening Network for Low-Light Image Enhancement

Li Wen Wang, Zhi Song Liu, Wan Chi Siu, Daniel P.K. Lun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

160 Citations (Scopus)


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.

Original languageEnglish
Article number9141197
Pages (from-to)7984-7996
Number of pages13
JournalIEEE Transactions on Image Processing
Publication statusPublished - Jul 2020


  • deep learning
  • image processing
  • Low-light image enhancement

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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