Low-light hazy scenes commonly appear at dusk and early morning. The visual enhancement for low-light hazy images is an ill-posed problem. Even though numerous methods have been proposed for image dehazing and low-light enhancement respectively, simply integrating them cannot deliver pleasing results for this particular task. In this paper, we present a novel method to enhance visibility for low-light hazy scenarios. To handle this challenging task, we propose two key techniques, namely cross-consistency dehazing-enhancement framework and physically based simulation for low-light hazy dataset. Specifically, the framework is designed for enhancing visibility of the input image via fully utilizing the clues from different sub-tasks. The simulation is designed for generating the dataset with ground-truths by the proposed low-light hazy imaging model. The extensive experimental results show that the proposed method outperforms the SOTA solutions on different metrics including SSIM (9.19%) and PSNR(5.03%). In addition, we conduct a user study on real images to demonstrate the effectiveness and necessity of the proposed method by human visual perception.

翻译：低照度雾霾场景常见于黄昏和清晨。低光照雾霾图像的视觉增强是一个病态问题。尽管目前已有大量方法分别用于图像去雾和低光增强，但简单整合这些方法无法为此特定任务带来令人满意的结果。本文提出了一种新颖的低光照雾霾场景可见度增强方法。为应对这一挑战性任务，我们提出了两项关键技术：交叉一致性去雾-增强框架和基于物理仿真的低光照雾霾数据集生成方法。具体而言，该框架通过充分利用不同子任务的线索来增强输入图像的可见度；仿真方法则基于所提出的低光照雾霾成像模型生成带有真实值的数据集。大量实验结果表明，所提方法在SSIM（提升9.19%）和PSNR（提升5.03%）等不同指标上均优于现有最优方案。此外，我们还在真实图像上进行了用户研究，通过人类视觉感知验证了所提方法的有效性和必要性。