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

    Yecai Guo1,2,*, Chen Li1,2, Qi Liu3

    CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 829-843, 2019, DOI: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… More >

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