TY  - EJOU
AU  - Guo, Yecai  
AU  - Li, Chen  
AU  - Liu, Qi  

TI  - R<sup>2</sup>N: A Novel Deep Learning Architecture for Rain Removal from Single Image
T2  - Computers, Materials \& Continua

PY  - 2019
VL  - 58
IS  - 3
SN  - 1546-2226

AB  - 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 (R<sup>2</sup>N), 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.
KW  - Deep learning
KW  -  convolution neural networks
KW  -  rain streaks
KW  -  single image de-raining
KW  -  skip connection

DO  - 10.32604/cmc.2019.03729