Abstract. Rain streaks attached to a camera may seriously affect the visibility of the background and considerably degrade image quality. We handle this problem by removing rain streaks to convert a rainy image into a clean one. This creates a problem in that the information with respect to the background of the occluded parts is close to being lost for the most part. To remove rain streaks from a single image, we employ a multi-scale aggregated generator network. Differing from previous generative adversarial networks, an enhanced generator block (EGB) is applied in the generator; this broadens the receptive field of the input features and enhances the attention to rain streaks details. In addition, both the generator and the discriminator networks contain the visual mask that learns the rain streak regions and their surroundings during training. By putting the mask forward, the generator network focuses on rain streaks and the surrounding regions and the discriminator network is capable of evaluating the local consistency of the restored regions.

中文翻译：

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使用多尺度聚合生成器网络进行图像去雨

摘要。相机上的雨痕可能会严重影响背景的可见度并显着降低图像质量。我们通过去除雨水条纹来将雨天图像转换为干净的图像来处理这个问题。这产生了一个问题，即关于被遮挡部分的背景的信息大部分都接近于丢失。为了从单个图像中去除雨水条纹，我们采用了多尺度聚合生成器网络。与之前的生成对抗网络不同，在生成器中应用了增强的生成器块（EGB）；这拓宽了输入特征的感受野，并增强了对雨水条纹细节的关注。此外，生成器和鉴别器网络都包含在训练过程中学习雨痕区域及其周围环境的视觉掩码。