Enhancing the visibility of nighttime hazy images is challenging due to the complex degradation distributions. Existing methods mainly address a single type of degradation (e.g., haze or low-light) at a time, ignoring the interplay of different degradation types and resulting in limited visibility improvement. We observe that the domain knowledge shared between low-light and haze priors can be reinforced mutually for better visibility. Based on this key insight, in this paper, we propose a novel framework that enhances visibility in nighttime hazy images by reinforcing the intrinsic consistency between haze and low-light priors mutually and progressively. In particular, our model utilizes image-, patch-, and pixel-level experts that operate across visual and frequency domains to recover global scene structure, regional patterns, and fine-grained details progressively. A frequency-aware router is further introduced to adaptively guide the contribution of each expert, ensuring robust image restoration. Extensive experiments demonstrate the superior performance of our model on nighttime dehazing benchmarks both quantitatively and qualitatively. Moreover, we showcase the generalizability of our model in daytime dehazing and low-light enhancement tasks.

翻译：夜间雾霾图像的可见度提升因复杂的退化分布而极具挑战性。现有方法主要针对单一退化类型（如雾霾或低光照）进行处理，忽略了不同退化类型间的相互作用，导致可见度改善有限。我们观察到，低光照与雾霾先验之间的领域知识可通过相互增强来提升可见度。基于这一关键发现，本文提出一种新颖框架，通过渐进式地相互增强雾霾与低光照先验的内在一致性，提升夜间雾霾图像的可见度。具体而言，我们的模型采用图像级、区块级和像素级专家模块，在视觉域与频域中协同操作，逐步恢复全局场景结构、区域模式和细粒度细节。进一步引入频率感知路由器，自适应引导各专家模块的贡献度，确保鲁棒的图像复原。大量实验证明，我们的模型在夜间去雾基准测试中，在定量与定性评估上均表现出优越性能。此外，我们展示了该模型在白天去雾与低光照增强任务中的泛化能力。