Deep residual skip connection neural network for single image deraining

Recently, deep neural networks have been successfully applied for removing rain streaks from images. However, these methods did not consider the relationships between the skip layers. In order to improve the deraining performance and use the information of different layers, we propose to construct connections between different layers and propose a residual skip connection neural network for single image deraining. Then we train the network on synthesized data. Both experiments on synthesized and real-world images show the proposed method outperforms state-of-the-art methods in terms of rain streak removal and image information preservation.