基于注意卷积网络的单幅图像中附着雾和雨滴去除
Adherent Mist and Raindrop Removal from a Single Image Using Attentive Convolutional Network
摘要：
温差引起的雾往往不均匀且模糊不清，附着在挡风玻璃、摄像机镜头等玻璃上，容易妨碍视线，严重影响图像质量。它们与附着的雨滴一起，给各种视觉系统带来了相当大的挑战，但却没有得到足够的重视。最近的其他类似问题的方法通常使用手工制作的先验来生成空间注意力地图。在这项工作中，我们新提出了一个问题的图像退化所造成的粘附雾和雨滴。采用注意卷积网络从单幅图像中去除附着的雾和雨滴。一个基线架构与一般的通道明智的注意力，空间的注意力，和多层次的功能融合。考虑到雾和雨滴的变化和区域特征，我们采用基于插值的注意力块来感知不同尺度的空间信息。实验结果表明，该方法可以从定性和定量两个方面改善严重退化图像的可见性。更多的应用实验表明，这个被低估的实际问题对于高级视觉场景至关重要。我们的方法在传统的去雾和纯雨滴问题上也达到了最先进的性能，除了我们处理附着的薄雾和雨滴的任务。
英文摘要：
Temperature difference-induced mist adhered to the glass, such as windshield, camera lens, is often inhomogeneous and obscure, easily obstructing the vision and severely degrading the image. Together with adherent raindrops, they bring considerable challenges to various vision systems but without enough attention. Recent methods for other similar problems typically use hand-crafted priors to generate spatial attention maps. In this work, we newly present a problem of image degradation caused by adherent mist and raindrops. An attentive convolutional network is adopted to visually remove the adherent mist and raindrop from a single image. A baseline architecture with general channel-wise attention, spatial attention, and multilevel feature fusion is used. Considering the variations and regional characteristics of adherent mist and raindrops, we apply interpolation-based pyramid-attention blocks to perceive spatial information at different scales. Experiments show that the proposed method can improve severely degraded images' visibility, both qualitatively and quantitatively. More applied experiments show that this underrated practical problem is critical to high-level vision scenes. Our method also achieves state-of-the-art performance on conventional dehazing and pure de-raindrop problems, in addition to our task of handling adherent mist and raindrops.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
