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.

翻译：现有基于深度学习的研究已广泛探索了白天图像去雾问题，但鲜有研究考虑夜间有雾场景的特性。夜间与白天雾霾存在两点差异：首先，夜间场景可能存在多个主动发光彩色光源且光照强度较低，这会导致具有局部化、耦合性和频率不一致特性的雾霾、光晕及噪声；其次，由于模拟数据与真实数据之间的域差异，将基于模拟数据训练的去雾模型应用于真实数据时可能出现非真实亮度。为解决上述两个问题，我们提出了一种面向真实夜间去雾的半监督模型。首先，通过空间注意力和频谱滤波实现空间-频率域信息交互模块以处理第一个问题。其次，设计了基于伪标签的重训练策略和基于局部窗口的亮度损失用于半监督训练过程，以抑制雾霾与光晕，同时实现真实亮度。公共基准上的实验验证了所提方法的有效性及其相较于现有最优方法的优越性。源代码与补充材料详见https://github.com/Xiaofeng-life/SFSNiD。