摘要
目前基于卷积神经网络的去雨方法,存在雨纹残留、图像模糊等问题。为此提出一种基于多尺度特征提取和循环残差注意的单幅图像去雨方法。通过构建多尺度拉普拉斯金字塔得到多尺度特征图,再设计循环残差注意模块加强阶段间联系、提取深度特征、增强重要特征权重,更好地去除雨纹并保留了图像细节。实验结果表明,该方法的去雨效果优于其他去雨算法。
At present,the rain removal methods based on convolution neural network still suffer from residual rain streaks and details lost.This paper proposes a novel network for single image deraining including the multi-scale feature extraction module and the recurrent residual attention.The multi-scale feature map was obtained by constructing the multi-scale Laplacian pyramid.The recurrent residual attention module was designed to promote the connection between stages,extract depth features and enhance the weight of important features,so as to better remove rain streak and preserve the image details.Experimental results demonstrate that the proposed method performs favorably against other state-of-the-art methods.
作者
刘邱铃
周刚
乔敏
Liu Qiuling;Zhou Gang;Qiao Min(Laboratory of Signal Detection and Processing,College of Information Science and Engineering,Xinjiang University,Urumqi 830000,Xinjiang,China)
出处
《计算机应用与软件》
北大核心
2025年第2期236-240,279,共6页
Computer Applications and Software
基金
国家自然科学基金项目(61603323,U1803261)
新疆维吾尔自治区自然科学基金项目(2021D01C057)。
关键词
卷积神经网络
单幅图像去雨
多层拉普拉斯金字塔
多尺度特征图
循环残差注意模块
Convolutional neural network
Single image deraining
Multi-layer Laplacian pyramid
Multi-scale feature map
Recurrent residual attention module
