舰船电子工程2025，Vol.45Issue(12)：30-35,6.DOI:10.3969/j.issn.1672-9730.2025.12.007
基于卷积神经网络和Swin Transformer的单幅图像去雨算法
A Single Image Deraining Algorithm Based on Convolutional Neural Networks and Swin Transformer
杜瑶 1薛涛 1李猛1
作者信息
- 1. 西安工程大学计算机科学学院 西安 710600
- 折叠
摘要
Abstract
Aiming at the issue of blurriness and detail loss in outdoor images captured during rainy days,this paper proposes a single-image deraining algorithm that leverages convolutional neural networks(CNN)and Transformer technology.Initially,a fea-ture extraction module that combines the local modeling strengths of CNNs with the global context capturing ability of the Swin Trans-former is developed.Subsequently,incorporating contrastive constraint loss,a comprehensive loss function is proposed to optimize network training.When compared to prevailing deraining algorithms,the proposed solution not only removes rain streaks more effec-tively,but also retains image details more efficiently.关键词
单幅图像去雨/卷积神经网络/Transformer/对比学习/注意力机制Key words
single image deraining/convolutional neural network/Transformer/contrastive learning/attention mechanism分类
信息技术与安全科学引用本文复制引用
杜瑶,薛涛,李猛..基于卷积神经网络和Swin Transformer的单幅图像去雨算法[J].舰船电子工程,2025,45(12):30-35,6.
