  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.
