title: Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration
summary: 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
paper_authors._id: 682fdc63bf762029ddcad451
paper_authors.name: Yuetong Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:16:39.979Z
paper_authors._id: 6640c647acae6bb179eedff5
paper_authors.avatarUrl: /avatars/bcaafaaa1d4b4c241d72a886401772e3.svg
paper_authors.fullname: Yuetong Liu
paper_authors.type: user
paper_authors.user: YuetongLiu
paper_authors._id: 682fdc63bf762029ddcad452
paper_authors.name: Yunqiu Xu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-23T04:01:10.107Z
paper_authors._id: 646c77911ee398a4e9404b8b
paper_authors.avatarUrl: /avatars/05d1ea421dd4f3e2fd47cbe99fc52933.svg
paper_authors.fullname: Yunqiu Xu
paper_authors.type: user
paper_authors.user: Yunqiu
paper_authors._id: 682fdc63bf762029ddcad453
paper_authors.name: Yang Wei
paper_authors._id: 682fdc63bf762029ddcad454
paper_authors.name: Xiuli Bi
paper_authors._id: 682fdc63bf762029ddcad455
paper_authors.name: Bin Xiao
paper_title: Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration
paper_summary: 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
paper_ai_summary: A unified framework for restoring nighttime images under diverse weather conditions using dual priors and adaptive collaboration.
paper_ai_keywords: Retinex-based dual priors illumination-aware degradation generation weather-aware dynamic specific-commonality collaboration nighttime image restoration