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
{this https URL}{\textit{this repository}}

提出了一种名为 UtilityIR 的基于退化类型和严重程度感知的模型，用于盲目进行全方位恶劣天气图像恢复，该方法在不同的天气恢复任务上，在客观和主观上都能明显超过其他方法，并拥有更少的模型参数。