In this paper, we address a problem of view-disturbing raindrop removal on a single image. In existing methods to tackle this problem, machine learning based ones seem promising but require elaborate pairwise images, i.e., the raindrop-degraded image and the corresponding clean image of the same scene, for training. To overcome this drawback, we propose a weakly supervised learning based model in the absence of pairwise training examples, which needs only a collection of images with image-level annotations indicating the presence/absence of raindrops for training. Specifically, we train a raindrop detector for highlighting regions of raindrops in a multi-task learning manner. Then, we propose an attention-based generative network for raindrop removal and introduce a weighted preservation loss to retain the non-raindrop details. Specially, our model can be mixedly trained with pairwise and unpaired samples, which enables us to conveniently adapt the model to a new domain. Experiments verify the effect of the proposed method. Especially, using only weakly-supervised learning, our method can achieve comparable results with state-of-the-art strongly-supervised learning methods.

中文翻译：

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单幅图像上雨滴去除的弱监督学习


在本文中，我们解决了单个图像上干扰视图的雨滴去除问题。在解决这个问题的现有方法中，基于机器学习的方法似乎很有前途，但需要精细的成对图像（即同一场景的雨滴退化图像和相应的干净图像）进行训练。为了克服这个缺点，我们在缺乏成对训练示例的情况下提出了一种基于弱监督学习的模型，该模型只需要带有图像级注释的图像集合来指示是否存在雨滴进行训练。具体来说，我们训练一个雨滴检测器，以多任务学习的方式突出显示雨滴区域。然后，我们提出了一种基于注意力的生成网络来去除雨滴，并引入加权保存损失来保留非雨滴细节。特别的是，我们的模型可以与成对和不成对样本混合训练，这使我们能够方便地将模型适应新的领域。实验验证了所提方法的效果。特别是，仅使用弱监督学习，我们的方法可以达到与最先进的强监督学习方法相当的结果。