  In this paper, we introduce a new perspective for improving image restoration
by removing degradation in the textual representations of a given degraded
image. Intuitively, restoration is much easier on text modality than image one.
For example, it can be easily conducted by removing degradation-related words
while keeping the content-aware words. Hence, we combine the advantages of
images in detail description and ones of text in degradation removal to perform
restoration. To address the cross-modal assistance, we propose to map the
degraded images into textual representations for removing the degradations, and
then convert the restored textual representations into a guidance image for
assisting image restoration. In particular, We ingeniously embed an
image-to-text mapper and text restoration module into CLIP-equipped
text-to-image models to generate the guidance. Then, we adopt a simple
coarse-to-fine approach to dynamically inject multi-scale information from
guidance to image restoration networks. Extensive experiments are conducted on
various image restoration tasks, including deblurring, dehazing, deraining, and
denoising, and all-in-one image restoration. The results showcase that our
method outperforms state-of-the-art ones across all these tasks. The codes and
models are available at \url{https://github.com/mrluin/TextualDegRemoval}.
