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Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S. and Cong, R. (2020) Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 13-19 June 2020, 1780-1789.
https://doi.org/10.1109/CVPR42600.2020.00185

has been cited by the following article:

• TITLE: Low-Light Enhancer for UAV Night Tracking Based on Zero-DCE++

  AUTHORS: Yihong Zhang, Yinjian Li, Qin Lin

  KEYWORDS: Low-Light Enhancement, Nighttime Tracking, Zero-DCE++, UAV Application

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.4, April 19, 2023

  ABSTRACT: Unmanned aerial vehicle (UAV) target tracking tasks can currently be successfully completed in daytime situations with enough lighting, but they are unable to do so in nighttime scenes with inadequate lighting, poor contrast, and low signal-to-noise ratio. This letter presents an enhanced low-light enhancer for UAV nighttime tracking based on Zero-DCE++ due to its ad-vantages of low processing cost and quick inference. We developed a light-weight UCBAM capable of integrating channel information and spatial features and offered a fully considered curve projection model in light of the low signal-to-noise ratio of night scenes. This method significantly improved the tracking performance of the UAV tracker in night situations when tested on the public UAVDark135 and compared to other cutting-edge low-light enhancers. By applying our work to different trackers, this search shows how broadly applicable it is.