Single image deraining is a challenging problem due to the presence of non-uniform rain densities and the ill-posedness of the problem. Moreover, over-/under-deraining can directly impact the performance of vision systems. To address these issues, we propose an end-to-end Context Aggregation Recurrent Network, called CARNet, to remove rain streaks from single images. In this paper, we assume that a rainy image is the linear combination of a clean background image with rain streaks and propose to take advantage of the context information and feature reuse to learn the rain streaks. In our proposed network, we first use the dilation technique to effectively aggregate context information without sacrificing the spatial resolution, and then leverage a gated subnetwork to fuse the intermediate features from different levels. To better learn and reuse rain streaks, we integrate a LSTM module to connect different recurrences for passing the information learned from the previous stages about the rain streaks to the following stage. Finally, to further refine the coarsely derained image, we introduce a refinement module to better preserve image details. As for the loss function, the L1-norm perceptual loss and SSIM loss are adopted to reduce the gridding artifacts caused by the dilated convolution. Experiments conducted on synthetic and real rainy images show that our CARNet achieves superior deraining performance both qualitatively and quantitatively over the state-of-the-art approaches.

由于不均匀的雨水密度和问题的不适性，单幅图像排空是一个具有挑战性的问题。此外，过度排水/排水不足会直接影响视觉系统的性能。为了解决这些问题，我们提出了一个端到端的上下文聚合循环网络，称为CARNet，以消除单个图像中的雨水条纹。在本文中，我们假设多雨图像是干净的背景图像与雨条纹的线性组合，并建议利用上下文信息和特征重用来学习雨条纹。在我们提出的网络中，我们首先使用膨胀技术在不牺牲空间分辨率的情况下有效地聚合上下文信息，然后利用门控子网融合不同级别的中间特征。为了更好地学习和重用雨水条纹，我们集成了一个LSTM模块以连接不同的重复发生，以便将从前一阶段学到的有关雨水条纹的信息传递到下一阶段。最后，为了进一步细化粗糙的图像，我们引入了一个细化模块以更好地保留图像细节。对于损失函数，采用L1范数感知损失和SSIM损失来减少由膨胀卷积引起的网格化伪影。在合成和真实的雨天图像上进行的实验表明，与最新方法相比，我们的CARNet在定性和定量方面均具有出色的排水性能。最后，为了进一步细化粗糙的图像，我们引入了一个细化模块以更好地保留图像细节。对于损失函数，采用L1范数感知损失和SSIM损失来减少由膨胀卷积引起的网格化伪影。在合成和真实的雨天图像上进行的实验表明，与最新方法相比，我们的CARNet在定性和定量方面均具有出色的排水性能。最后，为了进一步细化粗糙的图像，我们引入了一个细化模块以更好地保留图像细节。对于损失函数，采用L1范数感知损失和SSIM损失来减少由膨胀卷积引起的网格化伪影。在合成和真实的雨天图像上进行的实验表明，与最新方法相比，我们的CARNet在定性和定量方面均具有出色的排水性能。