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

• 作者: Chen Li Qi Liu Yecai Guo
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: DEEP learning CONVOLUTION NEURAL networks RAIN STREAKS single image deraining SKIP connection.
• 年，卷（期）: 2019,（3）
• 页码范围: 829-843
• 页数: 15页
• 分类号: TP3

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.