Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration
Authors: name: Yuetong Liu, name: Yunqiu Xu, name: Yang Wei, name: Xiuli Bi, name: Bin Xiao

Restoring nighttime images affected by multiple adverse weather conditions is
a practical yet under-explored research problem, as multiple weather conditions
often coexist in the real world alongside various lighting effects at night.
This paper first explores the challenging multi-weather nighttime image
restoration task, where various types of weather degradations are intertwined
with flare effects. To support the research, we contribute the AllWeatherNight
dataset, featuring large-scale high-quality nighttime images with diverse
compositional degradations, synthesized using our introduced illumination-aware
degradation generation. Moreover, we present ClearNight, a unified nighttime
image restoration framework, which effectively removes complex degradations in
one go. Specifically, ClearNight extracts Retinex-based dual priors and
explicitly guides the network to focus on uneven illumination regions and
intrinsic texture contents respectively, thereby enhancing restoration
effectiveness in nighttime scenarios. In order to better represent the common
and unique characters of multiple weather degradations, we introduce a
weather-aware dynamic specific-commonality collaboration method, which
identifies weather degradations and adaptively selects optimal candidate units
associated with specific weather types. Our ClearNight achieves
state-of-the-art performance on both synthetic and real-world images.
Comprehensive ablation experiments validate the necessity of AllWeatherNight
dataset as well as the effectiveness of ClearNight. Project page:
https://henlyta.github.io/ClearNight/mainpage.html