All-in-one adverse weather removal is an emerging topic on image restoration, which aims to restore multiple weather degradations in an unified model, and the challenge are twofold. First, discover and handle the property of multi-domain in target distribution formed by multiple weather conditions. Second, design efficient and effective operations for different degradations. To resolve this problem, most prior works focus on the multi-domain caused by different weather types. Inspired by inter\&intra-domain adaptation literature, we observe 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 propose a degradation type and severity aware model, called UtilityIR, for blind all-in-one bad weather image restoration. To extract weather information from single image, we propose a novel Marginal Quality Ranking Loss (MQRL) and utilize 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 outperform the state-of-the-art methods subjectively and objectively on different weather removal tasks with a large margin, and enjoy less model parameters. Proposed method even can restore unseen combined multiple degradation images, and modulate restoration level. Implementation code and pre-trained weights will be available at \url{https://github.com/fordevoted/UtilityIR}

翻译：全一体式恶劣天气去除是图像复原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，其挑战主要来自两方面：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，针对不同退化设计高效且有效的操作。为解决该问题，先前研究主要聚焦于不同天气类型引发的多域问题。通过借鉴域内外适应领域文献，我们观察到不仅天气类型，天气严重程度也会在每个天气类型域内引入多域特性——这一现象被前人方法忽视，并进一步限制了其性能。为此，本文提出一种退化类型与严重程度感知模型UtilityIR，用于盲式全一体式恶劣天气图像复原。为从单张图像中提取天气信息，我们设计了新颖的边缘质量排序损失（MQRL），并利用对比学习损失（CL）引导天气严重程度与类型的提取，同时采用多头交叉注意力（MHCA）和局部-全局自适应实例归一化（LG-AdaIN）等一系列创新技术，高效恢复空间变化的天气退化。所提方法在各类天气去除任务的主客观指标上均大幅超越现有最优方法，且模型参数量更少。该方法甚至能恢复未见过的组合多重退化图像，并调节复原程度。实现代码与预训练权重将发布于 \url{https://github.com/fordevoted/UtilityIR}。