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

翻译：雾霾、雨雪等恶劣天气条件会显著降低图像与视频的质量，对依赖视觉输入的智能交通系统构成严峻挑战。这些退化效应影响包括自动驾驶、交通监控与安防在内的关键应用。本综述系统回顾了为缓解天气引起的视觉损伤而开发的图像与视频复原技术。我们将现有方法归类为基于传统先验的方法和现代数据驱动模型，后者包括CNN、Transformer、扩散模型以及新兴的视觉-语言模型。复原策略根据其处理范围进一步划分为：单任务模型、多任务/多天气系统，以及能处理多种退化类型的全能框架。此外，我们探讨了昼夜复原的挑战、基准数据集和评估协议。综述最后深入讨论了当前研究的局限性，并展望了未来方向，如混合/复合退化复原、实时部署以及智能体AI框架。本工作旨在为推进智能交通环境中抗天气干扰的视觉系统发展提供有价值的参考。为紧跟该领域的快速发展，我们将通过https://github.com/ChaudharyUPES/A-comprehensive-review-on-Multi-weather-restoration 持续维护最新相关论文及其开源实现的定期更新。