@Article{cmc.2019.03729,
AUTHOR = {Yecai  Guo, Chen  Li, Qi  Liu},
TITLE = {R<sup>2</sup>N: A Novel Deep Learning Architecture for Rain Removal from Single Image},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {58},
YEAR = {2019},
NUMBER = {3},
PAGES = {829--843},
URL = {http://www.techscience.com/cmc/v58n3/23035},
ISSN = {1546-2226},
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 (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.},
DOI = {10.32604/cmc.2019.03729}
}