A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency
  Aware and Realistic Brightness Constraint
Xiaofeng Cong Jie Gui Jing Zhang Junming Hou Hao Shen
Existing research based on deep learning has extensively explored the problem
of daytime image dehazing. However, few studies have considered the
characteristics of nighttime hazy scenes. There are two distinctions between
nighttime and daytime haze. First, there may be multiple active colored light
sources with lower illumination intensity in nighttime scenes, which may cause
haze, glow and noise with localized, coupled and frequency inconsistent
characteristics. Second, due to the domain discrepancy between simulated and
real-world data, unrealistic brightness may occur when applying a dehazing
model trained on simulated data to real-world data. To address the above two
issues, we propose a semi-supervised model for real-world nighttime dehazing.
First, the spatial attention and frequency spectrum filtering are implemented
as a spatial-frequency domain information interaction module to handle the
first issue. Second, a pseudo-label-based retraining strategy and a local
window-based brightness loss for semi-supervised training process is designed
to suppress haze and glow while achieving realistic brightness. Experiments on
public benchmarks validate the effectiveness of the proposed method and its
superiority over state-of-the-art methods. The source code and Supplementary
Materials are placed in the https://github.com/Xiaofeng-life/SFSNiD.