In this study, the authors propose a novel network architecture to address the problem of removing rain streaks from single images. To strengthen the representational power of the network, they adopt the squeeze-and-excitation block in the network. Furthermore, they propose a new network connection called reusing original input (ROI). The ROI connection reuses the original input of the network and can provide more texture details of the background. These details can be useful for the restoration of the image after removing the rain streaks. Batch normalisation is applied to further improve the rain removal performance of the network. Despite the fact that the network is trained on synthetic data, experimental results show that the proposed network has a comparable performance on both synthetic images and real-world images to the state-of-the-art methods.

中文翻译：

---

重用原始输入的挤压和激励网络去除单个图像的雨水

在这项研究中，作者提出了一种新颖的网络体系结构来解决从单个图像中去除雨水条纹的问题。为了增强网络的代表性，他们在网络中采用了挤压和激励模块。此外，他们提出了一种新的网络连接，称为重用原始输入（ROI）。ROI连接可重用网络的原始输入，并可提供更多背景纹理细节。这些细节对于去除雨水条纹后的图像恢复很有用。应用批归一化以进一步提高网络的除雨性能。尽管该网络已经过综合数据培训，