Attentive Generative Adversarial Network for Raindrop Removal from A Single Image摘要翻译

文章题目：Attentive Generative Adversarial Network for Raindrop Removal from A Single Image

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摘要：

Raindrops adhered to a glass window or camera lens can severely hamper the visibility of a background scene and degrade an image considerably. In this paper, we address the problem by visually removing raindrops, and thus transforming a raindrop degraded image into a clean one. The problem is intractable, since first the regions occluded by raindrops are not given. Second, the information about the background scene of the occluded regions is completely lost for most part. To resolve the problem, we apply an attentive generative network using adversarial training. Our main idea is to inject visual attention into both the generative and discriminative networks. During the training, our visual attention learns about raindrop regions and their surroundings. Hence, by injecting this information, the generative network will pay more attention to the raindrop regions and the surrounding structures, and the discriminative network will be able to assess the local consistency of the restored regions. This injection of visual attention to both generative and discriminative networks is the main contribution of this paper. Our experiments show the effectiveness of our approach, which outperforms the state of the art methods quantitatively and qualitatively.

翻译：

粘附在玻璃窗或摄像机镜头上的雨滴会严重影响背景场景的可见性，并严重降低图像的质量。在这篇论文中，我们解决了这个问题，通过视觉去除雨滴，从而将雨滴退化的图像转化为清晰的图像。这个问题很棘手，因为首先没有给出被雨滴遮挡的区域。其次，被遮挡区域的背景场景信息在很大程度上是完全丢失的。为了解决这个问题，我们使用了一个注意力生成网络，使用了对抗性训练。我们的主要想法是将视觉注意力注入生成网络和区别网络。在训练中，我们的视觉注意力学习雨滴区域及其周围环境。因此，通过注入这些信息，生成网络将更加关注雨点区域和周围的结构，而鉴别网络将能够评估恢复区域的局部一致性。这种视觉注意对生成网络和区别网络的注入是本文的主要贡献。我们的实验证明了我们的方法的有效性，它在数量和质量上都超过了目前最先进的方法。