Purifying Low-light Images via Near-Infrared Enlightened Image

Renjie Wan, Boxin Shi*, Wenhan Yang, Bihan Wen, Ling-Yu Duan, Alex C. Kot

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


Cameras usually produce low-quality images under low-light conditions. Though many methods have been proposed to enhance the visibility of low-light images, they are mainly designed for illumination correction and less capable of sup-pressing the artifacts. In this paper, we propose to enhance the visibility and suppress artifacts by purifying low-light images under the guidance of the NIR enlightened image captured by using the near-infrared light as compensation. Specifically, we introduce a disentanglement framework to disentangle the structure and color components from the NIR enlightened and RGB images, respectively. Correspondingly, we introduce a new dataset with the RGB and NIR enlightened images for training and evaluation purposes. The experimental results show that our proposed method achieves promising results.
Original languageEnglish
Pages (from-to)8006-8019
Number of pages14
JournalIEEE Transactions on Multimedia
Early online date26 Dec 2022
Publication statusPublished - Dec 2023

Scopus Subject Areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Computer Science Applications

User-Defined Keywords

  • Cameras
  • Image color analysis
  • Image enhancement
  • Image restoration
  • Lighting
  • Surveillance
  • Training
  • Exposure correction
  • image enhancement
  • low-light image
  • near-infrared light


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