Raindrop blur or rain streaks can severely degrade the visual quality of the images, which causes many practical vision systems to fail to work, such as autonomous driving and video surveillance. Hence, it is important to address the problem of single image de-raining. In this paper, we propose a novel deep network for single image de-raining. The proposed network consists of three stages, including encoder stage, Dense Non-Local Residual Block (DNLRB) stage, and decoder stage. As spatial contextual information has been analyzed to be meaningful for image de-raining (Huang et al. ??), we adopt squeeze-and-excitation enhancing on feature maps in each convolution layer for capturing spatial contextual information. In addition, to better leverage spatial contextual information for extracting rain components, the non-local mean operation has been embed in DNLRB. Both quantitative and qualitative experimental results demonstrate the proposed method performs favorably against the state-of-the-art de-raining methods. The source codes will be available at https://supercong94.wixsite.com/supercong94.

雨滴模糊或雨水条纹会严重降低图像的视觉质量，从而导致许多实际的视觉系统无法正常工作，例如自动驾驶和视频监控。因此，重要的是解决单图像去水印的问题。在本文中，我们提出了一种新颖的用于单图像去雨水的深度网络。拟议的网络包括三个阶段，包括编码器阶段，密集非本地残留块（DNLRB）阶段和解码器阶段。由于已经分析了空间上下文信息对于图像去除雨水很有意义（Huang等人，??），我们在每个卷积层的特征图上采用了挤压和激发增强来捕获空间上下文信息。另外，为了更好地利用空间上下文信息来提取降雨分量，非本地均值运算已嵌入DNLRB中。定性和定量的实验结果均表明，所提出的方法与最新的除雨方法相比具有良好的性能。源代码将在https://supercong94.wixsite.com/supercong94上提供。