  Image super-resolution (SR) methods typically model degradation to improve
reconstruction accuracy in complex and unknown degradation scenarios. However,
extracting degradation information from low-resolution images is challenging,
which limits the model performance. To boost image SR performance, one feasible
approach is to introduce additional priors. Inspired by advancements in
multi-modal methods and text prompt image processing, we introduce text prompts
to image SR to provide degradation priors. Specifically, we first design a
text-image generation pipeline to integrate text into the SR dataset through
the text degradation representation and degradation model. The text
representation applies a discretization manner based on the binning method to
describe the degradation abstractly. This method maintains the flexibility of
the text and is user-friendly. Meanwhile, we propose the PromptSR to realize
the text prompt SR. The PromptSR utilizes the pre-trained language model (e.g.,
T5 or CLIP) to enhance restoration. We train the model on the generated
text-image dataset. Extensive experiments indicate that introducing text
prompts into SR, yields excellent results on both synthetic and real-world
images. Code is available at: https://github.com/zhengchen1999/PromptSR.
