论文标题
学习的双视图删除
Learned Dual-View Reflection Removal
论文作者
论文摘要
传统的反射删除算法要么使用单个图像作为输入，该输入遭受了内在的歧义，要么使用移动相机中的多个图像，这对用户来说是不便的。相反，我们提出了一种基于学习的反思算法，该算法使用立体声图像作为输入。这是两个极端之间的有效权衡：两种视图之间的视差为消除反射提供了线索，并且由于采用了智能手机中的立体声摄像头，因此很容易捕获两种观点。我们的模型由用于双视图注册的基于学习的反射不变流量模型，以及用于组合对齐图像对的学习合成模型。由于没有用于双视反射删除的数据集，因此我们提供了一个双视图的合成数据集，其中有没有反射用于培训。我们对立体对的其他现实世界数据集的评估表明，我们的算法优于现有的单像图像和多图像反射方法。
Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-off between the two extremes: the parallax between two views provides cues to remove reflections, and two views are easy to capture due to the adoption of stereo cameras in smartphones. Our model consists of a learning-based reflection-invariant flow model for dual-view registration, and a learned synthesis model for combining aligned image pairs. Because no dataset for dual-view reflection removal exists, we render a synthetic dataset of dual-views with and without reflections for use in training. Our evaluation on an additional real-world dataset of stereo pairs shows that our algorithm outperforms existing single-image and multi-image dereflection approaches.
