Low-light and hazy scenes often coexist, presenting challenges for visual enhancement in various tasks such as autonomous driving and video surveillance. Despite numerous methods proposed for image dehazing and low-light enhancement individually, their straightforward integration often fails to produce satisfactory results for this specific challenge. In this paper, we propose a novel approach to enhance visibility in low-light hazy scenarios. To tackle this formidable challenge, we introduce two key techniques: a cross-consistency dehazing-enhancement framework and a physically based simulation for generating a low-light hazy dataset. The framework is specifically designed to improve the visibility of input images by leveraging information from various sub-tasks, while the simulation is developed to create datasets with ground truth using the proposed low-light hazy imaging model. Extensive experimental results demonstrate that our method outperforms state-of-the-art solutions across various metrics, including a 9.19% increase in SSIM and a 5.03% increase in PSNR. Additionally, we conduct a user study on real images to underscore the effectiveness and necessity of our proposed method from the perspective of human visual perception.