Scene Text Image Super-Resolution (STISR) aims to enhance the resolution and
legibility of text within low-resolution (LR) images, consequently elevating
recognition accuracy in Scene Text Recognition (STR). Previous methods
predominantly employ discriminative Convolutional Neural Networks (CNNs)
augmented with diverse forms of text guidance to address this issue.
Nevertheless, they remain deficient when confronted with severely blurred
images, due to their insufficient generation capability when little structural
or semantic information can be extracted from original images. Therefore, we
introduce RGDiffSR, a Recognition-Guided Diffusion model for scene text image
Super-Resolution, which exhibits great generative diversity and fidelity even
in challenging scenarios. Moreover, we propose a Recognition-Guided Denoising
Network, to guide the diffusion model generating LR-consistent results through
succinct semantic guidance. Experiments on the TextZoom dataset demonstrate the
superiority of RGDiffSR over prior state-of-the-art methods in both text
recognition accuracy and image fidelity.

在场景文本图像超分辨率的研究中，通过引入一种生成模型 RGDiffSR 和一种去噪网络 RGDN，本文通过语义引导实现了场景文本图像的高保真度图像增强，从而在文本识别准确度和图像保真度方面都取得了优于现有方法的结果。