Abstract
We propose a new deep network architecture for removing rain streaks from individual images based on the deep
convolutional neural network (CNN). Inspired by the deep
residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to directly reduce the mapping range from input
to output, which makes the learning process easier. To further improve the de-rained result, we use a priori image domain knowledge by focusing on high frequency detail during training, which removes background interference and
focuses the model on the structure of rain in images. This
demonstrates that a deep architecture not only has benefits
for high-level vision tasks but also can be used to solve lowlevel imaging problems. Though we train the network on
synthetic data, we find that the learned network generalizes
well to real-world test images. Experiments show that the
proposed method significantly outperforms state-of-the-art
methods on both synthetic and real-world images in terms
of both qualitative and quantitative measures. We discuss
applications of this structure to denoising and JPEG artifact reduction at the end of the paper.