Image Super-Resolution with Text Prompt Diffusion

Zheng Chen, Yulun Zhang, Jinjin Gu, Xin Yuan, Linghe Kong, Guihai Chen, Xiaokang Yang

Keywords: Image Super-Resolution, Text Prompt, Diffusion Model

TL;DR: We introduce text prompts to enhance image super-resolution through a text-image generation pipeline and a diffusion model, PromptSR.

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 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 PromptSR 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. The code will be released.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 752