Image Super-Resolution with Text Prompt Diffusion.
CoRR(2023)
Abstract
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 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 representation method can also maintain the
flexibility of language. Meanwhile, we propose the PromptSR to realize the text
prompt SR. The PromptSR employs the diffusion model and the pre-trained
language model (e.g., T5 and CLIP). We train the model on the generated
text-image dataset. Extensive experiments indicate that introducing text
prompts into image SR, yields excellent results on both synthetic and
real-world images. Code: https://github.com/zhengchen1999/PromptSR.
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