R2N: A Novel Deep Learning Architecture for Rain Removal from Single Image  

作　　者：Yecai Guo Chen Li Qi Liu 

机构地区：[1]School of Electronic and Information Engineering,Nanjing University of Information Science and Technology,Nanjing Jiangsu,210044,China [2]Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing Jiangsu,210044,China [3]School of Computing,Edinburgh Napier University,Edinburgh,EH105DT,UK.

出　　处：《Computers, Materials & Continua》2019年第3期829-843,共15页计算机、材料和连续体（英文）

基　　金：This work was supported by the National Natural Science Foundation of China(Grant No.61673222);Jiangsu Universities Natural Science Research Project(Grant No.13KJA510001);Major Program of the National Social Science Fund of China(Grant No.17ZDA092).

摘　　要：Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems.Hence,it is necessary to address the problem of eliminating rain streaks from the individual rainy image.In this work,a deep convolution neural network(CNN)based method is introduced,called Rain-Removal Net(R2N),to solve the single image de-raining issue.Firstly,we decomposed the rainy image into its high-frequency detail layer and lowfrequency base layer.Then,we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its corresponding derained high-frequency detail layer.The CNN architecture consists of four convolution layers and four deconvolution layers,as well as three skip connections.The experiments on synthetic and real-world rainy images show that the performance of our architecture outperforms the compared state-of-the-art de-raining models with respects to the quality of de-rained images and computing efficiency.

关 键 词：Deep learning convolution neural networks rain streaks single image deraining skip connection. 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]