通过互相强化雾霾与低光先验，提升夜间雾霾图像清晰度。
Nighttime Hazy Image Enhancement via Progressively and Mutually Reinforcing Night-Haze Priors
- 设计多层级专家网络，分阶段恢复全局结构、区域模式与细节。
- 引入频域感知路由机制，自适应调节各专家贡献，增强恢复鲁棒性。
- 不仅提升夜间去雾效果，还适用于白天去雾和低光增强任务。
夜间雾霾图像的可见度增强因退化分布复杂而极具挑战。现有方法通常只针对单一退化类型（如雾霾或低光）进行处理，忽视了不同退化类型间的相互作用，导致可见度提升有限。我们观察到，低光与雾霾先验之间共享的领域知识可相互强化以改善可见度。基于此关键洞察，本文提出一种新框架，通过逐步且相互强化的内在一致性来提升夜间雾霾图像的可见度。具体而言，模型采用跨视觉与频域的图像级、块级和像素级专家，逐步恢复全局场景结构、局部模式与细粒度细节。进一步引入频域感知路由机制，自适应引导各专家贡献，确保图像重建的鲁棒性。大量实验证明，该模型在夜间去雾基准上均展现出优越的定量与定性性能。此外，还展示了其在日间去雾和低光增强任务中的泛化能力。
原文摘要 · Abstract (English)
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.
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