All-in-one adverse weather image restoration aims to remove multiple degradations, such as rain, haze, and snow, using a single unified model. Despite their broad applicability, existing methods typically compromise performance, delivering balanced but suboptimal results for individual degradation types. This issue becomes more pronounced when a domain gap exists between training and testing data. Motivated by the observation that modeling degradation patterns is more feasible than recovering clean content, we propose the Degradation Disentanglement and Transfer Network (DDTNet), which focuses specifically on degradation transfer. By disentangling degradation patterns from target-domain degraded images and transferring them to source domain clean images, DDTNet generates domain-adaptive paired training data. These pairs are then used to fine-tune restoration models, significantly enhancing their adaptability across diverse weather conditions and domains. The core of DDTNet is the Degradation Disentanglement Module (DDM), which comprises Degradation Coupled Attention (DCA) to capture both general and weather-specific features, thereby enabling effective disentanglement and transfer of degradation patterns. Experimental results demonstrate that DDTNet significantly and consistently improves existing all-in-one models across real-world deraining, desnowing, and dehazing datasets.

翻译：全合一恶劣天气图像复原旨在使用单一统一模型同时去除雨、雾、雪等多种退化。尽管具有广泛适用性，现有方法通常会折中性能，为各退化类型提供均衡但非最优的结果。当训练数据与测试数据存在域差距时，这一问题尤为突出。受退化模式建模比恢复清晰内容更具可行性的观察启发，我们提出退化解耦与迁移网络（DDTNet），该网络专门聚焦于退化迁移。通过将退化模式从目标域退化图像中解耦并迁移至源域清晰图像，DDTNet能够生成域自适应配对训练数据。这些配对数据随后用于微调复原模型，显著增强其在多样化天气条件和域间的适应能力。DDTNet的核心是退化解耦模块（DDM），该模块包含退化耦合注意力（DCA），用于捕捉通用特征与天气特定特征，进而实现退化模式的高效解耦与迁移。实验结果表明，DDTNet能够在真实场景的去雨、去雪和去雾数据集上显著且一致性地提升现有全合一模型的性能。