摘要
由于图像中的雨线条纹具有不同形状、尺寸且分布不均匀,单一神经网络学习分布不均匀的雨密度能力弱,去雨效果不显著,对此提出雨密度感知引导扩张网络对单张图片去除雨的方法。网络分为两部分:(1)雨密度感知网络对不同密度雨的图片进行分类(大雨、中雨、小雨);(2)联合雨密度感知分类信息引导扩张网络学习不同的雨密度特征细节,用于检测雨线和去雨。实验证明了该方法在合成和真实数据集上去雨的有效性。
Since the rain line stripes in the image have different shapes and sizes and are unevenly distributed,the rain density of the single neural network learning uneven distribution is weak,and the rain removal effect is not significant.This paper proposes a rain density sensing guide expansion network to remove rain from a single images.The network is divided into two parts.The first part is the rain density perception network classifying the images of different density rains(Heavy rain,Medium rain,Light rain).The second part is the expansion network guided by the joint rain density perception classification information learning different rain density characteristics details for detecting rain lines and removing rain.Experiments show the effectiveness of the method in the de-rain on synthetic and real data sets.
作者
安鹤男
张昌林
涂志伟
赵光军
刘佳
李蔚
An Henan;Zhang Changlin;Tu Zhiwei;Zhao Guangjun;Liu Jia;Li Wei(School of Electronic Science and Technology,Shenzhen University,Shenzhen 518061,China)
出处
《电子技术应用》
2019年第2期1-4,共4页
Application of Electronic Technique
关键词
单张图片
雨密度感知分类网络
扩张网络
去雨
single images
rain density classification network
expansion network
de-rain
