Single image deraining via deep pyramid network with spatial contextual information aggregation

Rain streaks usually give rise to visual degradation and cause many computer vision algorithms to fail. So it is necessary to develop an effective deraining algorithm as preprocess of high-level vision tasks. In this paper, we propose a novel deep learning based deraining method. Specifically, the multi-scale kernels and feature maps are both important for single image deraining. However, the previous works ignore the two multi-scale information or only consider the multi-scale kernels information. Instead, our method learns multi-scale information both from the perspectives of kernels and feature maps, respectively, by designing spatial contextual information aggregation module and pyramid network module. The former module can capture the rain streaks with different sizes and the latter module can extract rain streaks from different scales further. Moreover, we also employ squeeze-and-excitation and skip connections to enhance the correlation between channels and transmit the information from low-level to high-level, respectively. The experimental results show that the proposed method achieves significant improvements over the recent state-of-the-art methods in Rain100H, Rain100L, Rain1200 and Rain1400 datasets.