晴朗之夜：邁向多天氣條件下的夜間圖像復原

Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration

May 22, 2025

作者: Yuetong Liu, Yunqiu Xu, Yang Wei, Xiuli Bi, Bin Xiao

cs.AI

摘要

恢復受多種惡劣天氣條件影響的夜間圖像是一個實用但尚未充分探索的研究問題，因為在現實世界中，多種天氣條件往往與夜間的各種光照效果共存。本文首次探討了具有挑戰性的多天氣夜間圖像恢復任務，其中多種類型的天氣退化與光暈效應交織在一起。為了支持這項研究，我們貢獻了AllWeatherNight數據集，該數據集包含大規模高質量的夜間圖像，具有多種組合退化，這些圖像是使用我們引入的照明感知退化生成方法合成的。此外，我們提出了ClearNight，一個統一的夜間圖像恢復框架，能夠有效地一次性去除複雜的退化。具體來說，ClearNight提取基於Retinex的雙重先驗，並明確引導網絡分別關注不均勻照明區域和內在紋理內容，從而提高夜間場景中的恢復效果。為了更好地表示多種天氣退化的共同和獨特特徵，我們引入了一種天氣感知的動態特定-共性協作方法，該方法識別天氣退化並自適應地選擇與特定天氣類型相關的最佳候選單元。我們的ClearNight在合成圖像和真實世界圖像上均達到了最先進的性能。全面的消融實驗驗證了AllWeatherNight數據集的必要性以及ClearNight的有效性。項目頁面：https://henlyta.github.io/ClearNight/mainpage.html

English

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