计算机与现代化Issue(2)：100-107,8.DOI:10.3969/j.issn.1006-2475.2025.02.014
基于深度学习的视频去雨算法
Video Rain Removal Algorithm Based on Deep Learning
摘要
Abstract
In view of the fact that most traditional video rain removal algorithms only focus on removing rain marks and are trained only on synthetic data,ignoring more complex degradation factors such as rain accumulation,occlusion,and prior knowl-edge in real data.In this paper,we propose a two-stage video deraining algorithm that combines synthetic and real videos.The first stage algorithm performs a reverse recovery process under the guidance of the proposed rain removal model Initial-DerainNet.Continuous rain frames containing degradation factors are input into the network and physical prior knowledge is inte-grated to obtain an initial estimated rain-free frame.The second stage uses adversarial learning to refine the results,that is,to re-store the overall color,illumination distribution,etc.of the initially estimated rain-free frame to obtain a more accurate rain-free frame.Experimental results show that the PSNR value of this algorithm reaches 35.22 dB and the SSIM value reaches 0.9596 on the synthetic rain removal data set RainSyntheticDataset100,which is better than benchmark rain removal algorithms such as JORDER,DetailNet,SpacNN,SE,J4Rnet and FastDeRain.On the real rain video test set,the algorithm in this paper can achieve PNSR values of more than 30 dB on rain videos of different dimensions,which is better than other rain removal algo-rithms in terms of subjective visual effect and data metrics,and can effectively improve the quality of rainy day videos.关键词
视频去雨算法/物理先验恢复/生成对抗网络/退化因素Key words
video deraining algorithm/physics-based restoration/generative adversarial network/degradation factors分类
信息技术与安全科学引用本文复制引用
闫强,沈守婷,白俊卿,程国建..基于深度学习的视频去雨算法[J].计算机与现代化,2025,(2):100-107,8.基金项目
陕西省自然科学基金基础研究计划项目(2023-JC-YB-601) （2023-JC-YB-601）
西安市科技计划高校院所人才服务企业项目(23GXFW0077) （23GXFW0077）
西安石油大学研究生精品课程建设项目(2023-X-YKC-003) （2023-X-YKC-003）
西安欧亚学院科研基金资助项目(2024XJZK01) （2024XJZK01）
