Recursive modified dense network for single-image deraining

Guoqiang Chai, Zhaoba Wang, Guodong Guo, Youxing Chen, Yong Jin, Wei Wang, Xia Zhao

Abstract: Blurred images caused by rain streaks can degrade the performance of many computer vision algorithms. Therefore, the single-image rain removal problem has attracted tremendous interest. Although deep learning-based deraining methods have made significant progress, there are still many issues to be addressed in terms of improving the performance. We propose a recursive modified dense network for single-image deraining. As rain streaks have different sizes and shapes, contextual information is very important for rain removal. We use a dense network to extract image features and modify the network by removing all batch normalization layers. A simple deep network cannot completely remove rain streaks from the image, while increasing the network depth will make the computing more complicated. We take a dense block with loops to remove rain streaks stage by stage. Extensive experiments on both synthetic and real-world datasets show that the proposed method can achieve competitive results in comparison with the state-of-the-art methods for single-image rain removal.