计算机与现代化Issue(2)：86-93,8.DOI:10.3969/j.issn.1006-2475.2025.02.012
基于孪生特征融合网络的自然场景文本图像超分辨率方法
Twin Feature Fusion Network for Scene Text Image Super Resolution
摘要
Abstract
The aim of the scene text image super-resolution(STISR)method is to enhance the resolution and legibility of text im-ages,thereby improving the performance of downstream text recognition tasks.Previous studies have shown that the introduction of text-prior information can better guide the super-resolution.However,these methods have not effectively utilized text-prior in-formation and have not fully integrated it with image features,limiting super-resolution task performance.In this paper,we pro-pose a Twin Feature Fusion Network(TFFN)to address this problem.The method aims to maximize the utilization of text-prior information from pre-trained text recognizers,with a focus on the recovery of text area content.Firstly,text-prior information is extracted using a text recognition network.Next,a twin feature fusion module is constructed,which employs a twin attention mechanism to facilitate bidirectional interaction between image features and text-prior information.The fusion module further in-tegrates context-enhanced image features and text-prior information.Finally,sequence features are extracted to reconstruct the text image.Experiments on the benchmark TextZoom dataset show that the proposed TFFN improves the recognition accuracy of the ASTER,MORAN,and CRNN text recognition networks by 0.22～0.5,0.6～1.1 and 0.33～1.1 percentage points,respec-tively.关键词
图像超分辨率重建/文本图像/特征融合/自注意力机制/交叉注意力机制Key words
super-resolution reconstruction/text images/feature fusion/self-attention mechanism/cross-attention mechanism分类
信息技术与安全科学引用本文复制引用
冯心洁,王伟..基于孪生特征融合网络的自然场景文本图像超分辨率方法[J].计算机与现代化,2025,(2):86-93,8.基金项目
陕西省青年计划项目(2022JQ-624) （2022JQ-624）
中国高校产业研究创新基金资助项目(2021ALA02002) （2021ALA02002）
中国纺织工业协会高等教育教学改革研究项目(2021BKJGLX004) （2021BKJGLX004）
