  Most existing image restoration methods use neural networks to learn strong
image-level priors from huge data to estimate the lost information. However,
these works still struggle in cases when images have severe information
deficits. Introducing external priors or using reference images to provide
information also have limitations in the application domain. In contrast, text
input is more readily available and provides information with higher
flexibility. In this work, we design an effective framework that allows the
user to control the restoration process of degraded images with text
descriptions. We use the text-image feature compatibility of the CLIP to
alleviate the difficulty of fusing text and image features. Our framework can
be used for various image restoration tasks, including image inpainting, image
super-resolution, and image colorization. Extensive experiments demonstrate the
effectiveness of our method.
