论文标题
使用细心的卷积网络从单个图像中删除粘附的雾气和雨滴
Adherent Mist and Raindrop Removal from a Single Image Using Attentive Convolutional Network
论文作者
论文摘要
粘附在玻璃上的温度差引起的雾气，例如挡风玻璃，摄像机镜头，通常是不均匀的和晦涩的，很容易阻碍视力并严重降低图像。它们与粘附的雨滴一起，为各种视觉系统带来了巨大的挑战，但没有足够的关注。其他类似问题的最新方法通常使用手工制作的先验来生成空间注意图。在这项工作中，我们新提出了由粘附的雾和雨滴引起的图像降解问题。采用细心的卷积网络，以视觉上从单个图像中删除粘附的雾气和雨滴。使用具有一般频道注意力，空间注意力和多级特征融合的基线体系结构。考虑到粘附的雾和雨滴的变化和区域特征，我们将基于插值的金字塔注意区块应用于不同尺度的空间信息。实验表明，所提出的方法可以在定性和定量上改善严重降解的图像的可见性。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。我们的方法还可以在处理粘附的雾气和雨滴的任务外，还可以在传统的脱掩护和纯de-Raindrop问题上实现最先进的性能。
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.
