Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources. Existing nighttime dehazing methods often struggle with handling glow or low-light conditions, resulting in either excessively dark visuals or unsuppressed glow outputs. In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions. To handle glow effects, our framework learns from the rendered glow pairs. Specifically, a light source aware network is proposed to detect light sources of night images, followed by the APSF (Angular Point Spread Function)-guided glow rendering. Our framework is then trained on the rendered images, resulting in glow suppression. Moreover, we utilize gradient-adaptive convolution, to capture edges and textures in hazy scenes. By leveraging extracted edges and textures, we enhance the contrast of the scene without losing important structural details. To boost low-light intensity, our network learns an attention map, then adjusted by gamma correction. This attention has high values on low-light regions and low values on haze and glow regions. Extensive evaluation on real nighttime haze images, demonstrates the effectiveness of our method. Our experiments demonstrate that our method achieves a PSNR of 30.72dB, outperforming state-of-the-art methods by 14$\%$ on GTA5 nighttime haze dataset. Our data and code is available at: \url{https://github.com/jinyeying/nighttime_dehaze}.

翻译：夜间雾霾场景中的可见度常因多种因素降低，包括低光照、强烈辉光、光散射以及多色光源的存在。现有夜间去雾方法在处理辉光或低光照条件时往往效果不佳，导致图像要么过暗，要么辉光无法被有效抑制。本文通过抑制辉光和增强低光照区域，提升单幅夜间雾霾图像的可见度。为处理辉光效应，我们的框架从渲染的辉光图像对中学习。具体而言，我们提出一种光源感知网络来检测夜间图像中的光源，随后通过APSF（角点扩散函数）引导的辉光渲染进行辉光呈现。该框架在渲染图像上训练，从而实现辉光抑制。此外，我们利用梯度自适应卷积来捕获雾霾场景中的边缘和纹理。通过提取的边缘和纹理，我们增强场景对比度而不丢失重要结构细节。为提升低光照强度，网络学习注意力图，并通过伽马校正进行调整。该注意力图在低光照区域具有高响应值，而在雾霾和辉光区域响应值较低。在真实夜间雾霾图像上的广泛评估验证了方法的有效性。实验表明，我们的方法在GTA5夜间雾霾数据集上达到30.72dB的PSNR，比现有最优方法提升14%。数据和代码已开源：\url{https://github.com/jinyeying/nighttime_dehaze}。