Images captured in snowy days suffer from noticeable degradation of scene visibility, which degenerates the performance of current vision-based intelligent systems. Removing snow from images thus is an important topic in computer vision. In this paper, we propose a Deep Dense Multi-Scale Network (DDMSNet) for snow removal by exploiting semantic and depth priors. As images captured in outdoor often share similar scenes and their visibility varies with depth from camera, such semantic and depth information provides a strong prior for snowy image restoration. We incorporate the semantic and depth maps as input and learn the semantic-aware and geometry-aware representation to remove snow. In particular, we first create a coarse network to remove snow from the input images. Then, the coarsely desnowed images are fed into another network to obtain the semantic and depth labels. Finally, we design a DDMSNet to learn semantic-aware and geometry-aware representation via a self-attention mechanism to produce the final clean images. Experiments evaluated on public synthetic and real-world snowy images verify the superiority of the proposed method, offering better results both quantitatively and qualitatively. https://github.com/HDCVLab/Deep-Dense-Multi-scale-Network https://github.com/HDCVLab/Deep-Dense-Multi-scale-Network.

中文翻译：

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使用语义和深度先验的深度密集多尺度网络除雪


在雪天拍摄的图像的场景可见度明显下降，这降低了当前基于视觉的智能系统的性能。因此，从图像中去除雪是计算机视觉中的一个重要课题。在本文中，我们提出了一种利用语义和深度先验进行除雪的深度密集多尺度网络（DDMSNet）。由于在室外拍摄的图像通常共享相似的场景，并且它们的可见度随着相机的深度而变化，因此这种语义和深度信息为雪地图像恢复提供了强大的先验。我们将语义和深度图作为输入，并学习语义感知和几何感知表示来去除积雪。特别是，我们首先创建一个粗网络来从输入图像中去除雪。然后，将粗略除雪的图像输入另一个网络以获得语义和深度标签。最后，我们设计了一个 DDMSNet，通过自注意力机制学习语义感知和几何感知表示，以生成最终的干净图像。对公共合成和真实雪景图像进行评估的实验验证了所提出方法的优越性，在定量和定性方面提供了更好的结果。 https://github.com/HDCVLab/Deep-Dense-Multi-scale-Network https://github.com/HDCVLab/Deep-Dense-Multi-scale-Network。