Scene text image super-resolution (STISR) aims at simultaneously increasing the resolution and readability of low-resolution scene text images, thus boosting the performance of the downstream recognition task. Two factors in scene text images, semantic information and visual structure, affect the recognition performance significantly. To mitigate the effects from these factors, this paper proposes a Prior-Enhanced Attention Network (PEAN). Specifically, a diffusion-based module is developed to enhance the text prior, hence offering better guidance for the SR network to generate SR images with higher semantic accuracy. Meanwhile, the proposed PEAN leverages an attention-based modulation module to understand scene text images by neatly perceiving the local and global dependence of images, despite the shape of the text. A multi-task learning paradigm is employed to optimize the network, enabling the model to generate legible SR images. As a result, PEAN establishes new SOTA results on the TextZoom benchmark. Experiments are also conducted to analyze the importance of the enhanced text prior as a means of improving the performance of the SR network. Code will be made available at https://github.com/jdfxzzy/PEAN.

本文提出了一种Prior-Enhanced Attention Network（PEAN），通过扩散模块增强文本先验信息，以提高语义准确性，并利用基于注意力调制的模块来理解场景文本图像，从而生成可读的超分辨率图像。在TextZoom基准测试中，PEAN取得了新的最先进结果。

基于扩散的先验增强注意力网络用于场景文本图像超分辨率