Open Access

ARTICLE

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

Yecai Guo^1,2,*, Chen Li^1,2, Qi Liu³

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, EH10 5DT, UK.

* Corresponding Author: Yecai Guo. Email: .

Computers, Materials & Continua 2019, 58(3), 829-843. https://doi.org/10.32604/cmc.2019.03729

Abstract

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 low-frequency 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 de-rained 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.

---

Keywords

---

Citations