Deep convolutional neural networks (CNNs) have shown their advantages in the single image de-raining task. However, most existing CNNs-based methods utilize only local spatial information without considering long-range contextual information. In this paper, we propose a graph convolutional networks (GCNs)-based model to solve the above problem. We specifically design two graphs to extract representations from new dimensions. The first graph models the global spatial relationship between pixels in the feature, while the second graph models the interrelationship across the channels. By integrating conventional CNNs and our GCNs into a single framework, the proposed method is able to explore comprehensive feature representations from three aspects, i.e., local spatial patterns, global spatial coherence and channel correlation. To better exploit the explored rich feature representations, we further introduce a simple yet effective recurrent operations to perform the de-raining process in a successive manner. Benefiting from the rich information exploration and exploitation, our method achieves state-of-the-art results on both synthetic and real-world data sets.

深度卷积神经网络（CNN）在单图像消除雨水任务中显示了其优势。但是，大多数现有的基于CNN的方法都只使用局部空间信息，而不考虑远程上下文信息。在本文中，我们提出了一种基于图卷积网络（GCN）的模型来解决上述问题。我们专门设计了两个图来从新维度中提取表示。第一个图建模特征中像素之间的全局空间关系，而第二个图建模跨通道的相互关系。通过将传统的CNN和我们的GCN集成到一个框架中，该方法能够从局部空间模式，全局空间相干性和信道相关性三个方面探索综合特征表示。为了更好地利用探索的丰富特征表示，我们进一步介绍了一个简单而有效的循环操作，以连续方式执行除雨过程。受益于丰富的信息探索和开发，我们的方法在综合和真实数据集上均达到了最新的结果。