南京邮电大学学报（自然科学版）2025，Vol.45Issue(5)：57-65,9.DOI:10.14132/j.cnki.1673-5439.2025.05.007
单幅图像去模糊的多尺度特征提取和融合网络
Multi-scale feature extraction and fusion network for single image deblurring
摘要
Abstract
Significant advancements have been made in image deblurring through multi-layer networks,but their performance remains limited by challenges in feature extraction and residual connections.To ad-dress these issues,this paper proposes a multi-scale feature extraction and fusion network(MSFN)for image deblurring.The core idea of the network is to enhance image feature extraction through multi-scale inputs and outputs.Further,MSFN utilizes its feature adaptive detail enhancement(ADE)modules and cross-scale feature fusion(CSFF)modules to capture multi-scale features at different network depths,thereby optimizing the residual connection process and effectively integrating multi-scale information.Ex-perimental results demonstrate that the proposed algorithm achieves superiority in quantitative analysis and significantly improves subjective visual effects,exhibiting an advanced performance.关键词
图像去模糊/深度学习/多尺度/细节增强/特征融合Key words
image deblurring/deep learning/multiple scale/detail enhancement/feature fusion分类
信息技术与安全科学引用本文复制引用
武婷婷,万少杰..单幅图像去模糊的多尺度特征提取和融合网络[J].南京邮电大学学报（自然科学版）,2025,45(5):57-65,9.基金项目
国家自然科学基金(61971234)和江苏省研究生科研与实践创新计划项目(KYCX23_0960)资助项目 （61971234）
