Multi-Scale Feature Guided Low-Light Image Enhancement

Lanqing Guo, Renjie Wan, Guan Ming Su, Alex C. Kot, Bihan Wen*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

8 Citations (Scopus)


Low-light image enhancement aims at enlarging the intensity of image pixels to better match human perception and to improve the performance of subsequent vision tasks. While it is relatively easy to enlighten a globally low-light image, the lighting condition of realistic scenes is usually non-uniform and complex, e.g., some images may contain both bright and extremely dark regions, with or without rich features and information. Existing methods often generate abnormal light-enhancement results with over-exposure artifacts without proper guidance. To tackle this challenge, we propose a multi-scale feature guided attention mechanism in the deep generator, which can effectively perform a spatially-varying light enhancement. The attention map is fused by both the gray map and extracted feature map of the input image, to focus more on those dark and informative regions. Our baseline is an unsupervised generative adversarial network, which can be trained without any low/normal-light image pair. Experimental results demonstrate the superiority in visual quality and performance of subsequent object detection over state-of-the-art alternatives.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings
Number of pages5
ISBN (Electronic)9781665441155
ISBN (Print)9781665431026
Publication statusPublished - 19 Sept 2021
Event2021 IEEE International Conference on Image Processing, ICIP 2021 - Anchorage, United States
Duration: 19 Sept 202122 Sept 2021

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549


Conference2021 IEEE International Conference on Image Processing, ICIP 2021
Country/TerritoryUnited States
Internet address

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

User-Defined Keywords

  • Attention
  • Generative Adversarial Network (GAN)
  • Low-Light
  • Unsupervised Learning


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