Rain streaks, particularly in heavy rain, not only degrade visibility but also make many computer vision algorithms fail to function properly. In this paper, we address this visibility problem by focusing on single-image rain removal, even in the presence of dense rain streaks and rain-streak accumulation, which is visually similar to mist or fog. To achieve this, we introduce a new rain model and a deep learning architecture. Our rain model incorporates a binary rain map indicating rain-streak regions, and accommodates various shapes, directions, and sizes of overlapping rain streaks, as well as rain accumulation, to model heavy rain. Based on this model, we construct a multi-task deep network, which jointly learns three targets: the binary rain-streak map, rain streak layers, and clean background, which is our ultimate output. To generate features that can be invariant to rain steaks, we introduce a contextual dilated network, which is able to exploit regional contextual information. To handle various shapes and directions of overlapping rain streaks, our strategy is to utilize a recurrent process that progressively removes rain streaks. Our binary map provides a constraint and thus additional information to train our network. Extensive evaluation on real images, particularly in heavy rain, shows the effectiveness of our model and architecture.

中文翻译：

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通过上下文深化网络从单个图像进行联合降雨检测和去除。

降雨条纹，尤其是在大雨中，不仅降低了能见度，而且使许多计算机视觉算法无法正常运行。在本文中，我们通过着重于单图像去除雨水来解决此可见性问题，即使在存在密集的雨水条纹和雨水条纹积聚的情况下（视觉上类似于雾或雾）也是如此。为了实现这一目标，我们引入了新的降雨模型和深度学习架构。我们的降雨模型结合了指示降雨区域的二进制降雨图，并可以适应各种形状，方向和大小的重叠雨条以及降雨积聚，从而对大雨进行建模。基于此模型，我们构建了一个多任务深度网络，该网络可以共同学习三个目标：二进制雨条纹图，雨条纹层和干净的背景，这是我们的最终输出。为了生成雨牛排不变的特征，我们引入了一个上下文扩张的网络，该网络能够利用区域上下文信息。为了处理重叠的雨条的各种形状和方向，我们的策略是利用循环过程逐步消除雨条。我们的二进制映射图提供了一个约束，因此提供了更多信息来训练我们的网络。对真实图像的广泛评估，尤其是在大雨中，显示了我们的模型和体系结构的有效性。我们的二进制映射图提供了一个约束，因此提供了更多信息来训练我们的网络。对真实图像的广泛评估，尤其是在大雨中，显示了我们的模型和体系结构的有效性。我们的二进制映射图提供了一个约束，因此提供了更多信息来训练我们的网络。对真实图像的广泛评估，尤其是在大雨中，显示了我们的模型和体系结构的有效性。