All-in-one adverse weather removal is an emerging topic on image restoration, which aims to restore multiple weather degradation in an unified model, and the challenging are twofold. First, discovering and handling the property of multi-domain in target distribution formed by multiple weather conditions. Second, design efficient and effective operations for different degradation types. To address this problem, most prior works focus on the multi-domain caused by weather type. Inspired by inter\&intra-domain adaptation literature, we observed that not only weather type but also weather severity introduce multi-domain within each weather type domain, which is ignored by previous methods, and further limit their performance. To this end, we proposed a degradation type and severity aware model, called \textbf{UtilityIR}, for blind all-in-one bad weather image restoration. To extract weather information from single image, we proposed a novel Marginal Quality Ranking Loss (MQRL) and utilized Contrastive Loss (CL) to guide weather severity and type extraction, and leverage a bag of novel techniques such as Multi-Head Cross Attention (MHCA) and Local-Global Adaptive Instance Normalization (LG-AdaIN) to efficiently restore spatial varying weather degradation. The proposed method can significantly outperform the SOTA methods subjectively and objectively on different weather restoration tasks with a large margin, and enjoy less model parameters. Proposed method even can restore \textbf{unseen} domain combined multiple degradation images, and modulating restoration level. Implementation code will be available at {https://github.com/fordevoted/UtilityIR}{\textit{this repository}}

翻译：全能型恶劣天气去除是图像恢复领域的一个新兴课题，旨在通过统一模型恢复多种天气退化效果，其挑战性体现在两个方面：第一，发现并处理由多种天气条件形成的目标分布中的多域特性；第二，针对不同退化类型设计高效且有效的操作方式。为解决此问题，先前的大多数研究聚焦于天气类型导致的多域现象。受域间与域内自适应文献启发，我们观察到不仅天气类型，天气严重程度也会在每种天气类型域内引入多域特性——这一现象被先前方法所忽视，并进一步限制了其性能。为此，我们提出了一种退化类型与严重程度感知模型，命名为**UtilityIR**，用于盲式全能型恶劣天气图像恢复。为从单张图像中提取天气信息，我们创新性地引入了边缘质量排序损失函数（MQRL），并利用对比损失（CL）引导天气严重程度与类型的提取；同时采用多头部交叉注意力（MHCA）和全局-局部自适应实例归一化（LG-AdaIN）等系列新技术，高效恢复空间变化的天气退化。所提方法在不同天气恢复任务的主客观评价指标上均显著超越现有最优方法（SOTA），且模型参数量更少。该方法甚至能够恢复包含多种退化效果的**未见域**图像，并可调节恢复程度。实现代码将发布于{https://github.com/fordevoted/UtilityIR}{\textit{本仓库}}。