Single image deraining is one of challenges in image processing. An efficient algorithm for single image deraining can significantly improve the image quality in severe weather conditions. Existing deraining algorithms only pay attention to spatial characteristics or channel information, which leads to poor performance of the network. In this paper, we propose a novel dual attention residual group network (DARGNet) to get better deraining performance. Specifically, the framework of dual attention includes spatial attention and channel attention. The spatial attention can extract the multi-scale feature to adapt to different shapes and size of the rain streaks. Meanwhile, channel attention has established the dependence relationship among different channels. In addition, in order to simplify the structure, we integrate the dual attention module and convolution layers into the residual groups, which also improves information transmission. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed network achieves a good effect of deraining tasks. The source code is available at https://github.com/zhanghai404.

单幅图像去雨是图像处理中的挑战之一。一种有效的单幅图像去雨算法可以显着提高恶劣天气条件下的图像质量。现有的去雨算法只关注空间特征或信道信息，导致网络性能不佳。在本文中，我们提出了一种新颖的双重注意残差组网络（DARGNet）以获得更好的去雨性能。具体来说，双重注意力的框架包括空间注意力和通道注意力。空间注意力可以提取多尺度特征以适应不同形状和大小的雨条纹。同时，渠道注意力建立了不同渠道之间的依赖关系。此外，为了简化结构，我们将双重注意力模块和卷积层集成到残差组中，这也改善了信息传输。对合成数据集和真实数据集的大量实验表明，所提出的网络在去除任务方面取得了良好的效果。源代码可在 https://github.com/zhanghai404 获得。