Multi-task image restoration has gained significant interest due to its
inherent versatility and efficiency compared to its single-task counterpart.
Despite its potential, performance degradation is observed with an increase in
the number of tasks, primarily attributed to the distinct nature of each
restoration task. Addressing this challenge, we introduce
\mbox{\textbf{DINO-IR}}, a novel multi-task image restoration approach
leveraging robust features extracted from DINOv2. Our empirical analysis shows
that while shallow features of DINOv2 capture rich low-level image
characteristics, the deep features ensure a robust semantic representation
insensitive to degradations while preserving high-frequency contour details.
Building on these features, we devise specialized components, including
multi-layer semantic fusion module, DINO-Restore adaption and fusion module,
and DINO perception contrastive loss, to integrate DINOv2 features into the
restoration paradigm. Equipped with the aforementioned components, our DINO-IR
performs favorably against existing multi-task image restoration approaches in
various tasks by a large margin, indicating the superiority and necessity of
reinforcing the robust features for multi-task image restoration.

我们引入 DINO-IR，一种新颖的多任务图像修复方法，利用从 DINOv2 提取的稳健特征，通过使用浅层特征捕捉低级图像特征、深层特征保证鲁棒语义表征且不敏感于退化、并使用专门的组件进行特征集成，包括多层语义融合模块、DINO-Restore 调适与融合模块以及 DINO 感知对比损失，该方法在各种任务上优于现有的多任务图像修复方法，凸显了增强鲁棒特征对于多任务图像修复的优越性和必要性。