Rddan: A Residual Dense Dilated Aggregated Network For Single Image Deraining

Rainy images contain rain streaks with different sizes, shapes, directions, and densities. To efficiently remove rain streaks from rainy images, it is necessary to capture rich rain details. In this paper, we propose a Residual Dense Dilated Aggregated Network (RDDAN) to focus on different types of rain steaks and efficiently model rain distribution from rainy images. Specifically, a Residual Dense Dilated Aggregated Block (RDDAB) is constructed to fully extract and exploit rain details hierarchically. In RDDAB, dilated aggregated module is applied to capture multi-scale rain details, dense connection is employed to fully exploit hierarchical features extracted by dilated aggregated module, and residual connection is introduced to keep flow of rain details among different blocks. Besides, all the features extracted by each RDDAB are fused progressively which allows the network to adaptively focus on significant hierarchical features inter blocks. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on synthetic and real-world datasets. The source code is available at https://github.com/nnUyi/RDDAN.