Low-Light Image Enhancement via a Deep Hybrid Network

Wenqi Ren, Sifei Liu, Lin Ma, Qianqian Xu, Xiangyu Xu, Xiaochun Cao, Junping Du, Ming Hsuan Yang

Research output: Contribution to journalArticle

18 Citations (Scopus)


Camera sensors often fail to capture clear images or videos in a poorly lit environment. In this paper, we propose a trainable hybrid network to enhance the visibility of such degraded images. The proposed network consists of two distinct streams to simultaneously learn the global content and the salient structures of the clear image in a unified network. More specifically, the content stream estimates the global content of the low-light input through an encoder-decoder network. However, the encoder in the content stream tends to lose some structure details. To remedy this, we propose a novel spatially variant recurrent neural network (RNN) as an edge stream to model edge details, with the guidance of another auto-encoder. The experimental results show that the proposed network favorably performs against the state-of-the-art low-light image enhancement algorithms.

Original languageEnglish
Article number8692732
Pages (from-to)4364-4375
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number9
Publication statusPublished - 2019 Sep

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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    Ren, W., Liu, S., Ma, L., Xu, Q., Xu, X., Cao, X., Du, J., & Yang, M. H. (2019). Low-Light Image Enhancement via a Deep Hybrid Network. IEEE Transactions on Image Processing, 28(9), 4364-4375. [8692732]. https://doi.org/10.1109/TIP.2019.2910412