  This work aims to tackle the all-in-one image restoration task, which seeks
to handle multiple types of degradation with a single model. The primary
challenge is to extract degradation representations from the input degraded
images and use them to guide the model's adaptation to specific degradation
types. Recognizing that various degradations affect image content differently
across frequency bands, we propose a new all-in-one image restoration approach
from a frequency perspective, leveraging advanced vision transformers. Our
method consists of two main components: a frequency-aware Degradation prior
learning transformer (Dformer) and a degradation-adaptive Restoration
transformer (Rformer). The Dformer captures the essential characteristics of
various degradations by decomposing inputs into different frequency components.
By understanding how degradations affect these frequency components, the
Dformer learns robust priors that effectively guide the restoration process.
The Rformer then employs a degradation-adaptive self-attention module to
selectively focus on the most affected frequency components, guided by the
learned degradation representations. Extensive experimental results demonstrate
that our approach outperforms the existing methods on four representative
restoration tasks, including denoising, deraining, dehazing and deblurring.
Additionally, our method offers benefits for handling spatially variant
degradations and unseen degradation levels.
