Abstract
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.
Abstract (translated)
夜间模糊图像的清晰度增强由于复杂的退化分布而具有挑战性。现有的方法主要一次解决一种类型的退化（例如，雾或低光），忽略了不同退化类型之间的相互作用，导致可见性的提升有限。我们观察到，低光和雾先验之间共享的领域知识可以通过相互强化来提高更好的可视性。基于这一关键见解，在本文中，我们提出了一种新的框架，通过在夜间模糊图像上相互且逐步地增强雾和低光先验之间的内在一致性来提升可见度。 具体来说，我们的模型利用了跨视觉和频率域工作的图像级、补丁级和像素级专家，以逐步恢复全局场景结构、区域模式和细粒度细节。此外，我们引入了一个频率感知路由器，以自适应地指导每个专家的贡献，确保稳健的图像修复。广泛的实验表明，在夜间去雾基准测试中，我们的模型在定量和定性上都表现出优越的性能。此外，我们展示了我们的模型在白天去雾和低光增强任务中的泛化能力。
URL
https://arxiv.org/abs/2601.01998
