综述多天气图像恢复技术，助力智能交通视觉系统抗干扰
Clear Roads, Clear Vision: Advancements in Multi-Weather Restoration for Smart Transportation
- 按传统先验与数据驱动分类，涵盖CNN、Transformer等模型
- 提出单任务、多任务及一体化框架，支持多种天气退化统一处理
- 适合研究智能交通视觉鲁棒性的学者与工程开发者
恶劣天气如雾霾、雨雪会严重降低图像和视频质量，对依赖视觉输入的智能交通系统（ITS）构成严峻挑战，影响自动驾驶、交通监控与安防等关键应用。本文全面综述了用于缓解天气导致视觉退化的图像与视频恢复技术。将现有方法分为基于传统先验的方法与现代数据驱动模型，包括卷积神经网络（CNN）、Transformer、扩散模型以及新兴的视觉-语言模型（VLMs）。根据处理范围进一步划分为单任务模型、多任务/多天气系统以及可应对多样化退化的全功能框架。此外，讨论昼夜恢复差异、基准数据集与评估协议。最后深入分析当前研究局限，并展望未来方向：混合/复合退化恢复、实时部署及代理式人工智能框架。本工作旨在为提升智能交通环境中的天气鲁棒视觉系统提供参考。为持续追踪该领域进展，我们将定期更新相关论文及其开源实现，详见https://github.com/ChaudharyUPES/A-comprehensive-review-on-Multi-weather-restoration。
原文摘要 · Abstract (English)
Adverse weather conditions such as haze, rain, and snow significantly degrade the quality of images and videos, posing serious challenges to intelligent transportation systems (ITS) that rely on visual input. These degradations affect critical applications including autonomous driving, traffic monitoring, and surveillance. This survey presents a comprehensive review of image and video restoration techniques developed to mitigate weather-induced visual impairments. We categorize existing approaches into traditional prior-based methods and modern data-driven models, including CNNs, transformers, diffusion models, and emerging vision-language models (VLMs). Restoration strategies are further classified based on their scope: single-task models, multi-task/multi-weather systems, and all-in-one frameworks capable of handling diverse degradations. In addition, we discuss day and night time restoration challenges, benchmark datasets, and evaluation protocols. The survey concludes with an in-depth discussion on limitations in current research and outlines future directions such as mixed/compound-degradation restoration, real-time deployment, and agentic AI frameworks. This work aims to serve as a valuable reference for advancing weather-resilient vision systems in smart transportation environments. Lastly, to stay current with rapid advancements in this field, we will maintain regular updates of the latest relevant papers and their open-source implementations at https://github.com/ChaudharyUPES/A-comprehensive-review-on-Multi-weather-restoration
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