学习使用分层分解透视障碍物
Learning to See Through Obstructions with Layered Decomposition
摘要：
我们提出了一种基于学习的方法，用于从移动相机捕获的短序列图像中去除不需要的障碍物，例如窗口反射，围栏遮挡或粘附的雨滴。我们的方法利用背景和阻碍元素之间的运动差异来恢复这两个层。具体来说，我们交替估计两层的密集光流场和通过深度卷积神经网络从流扭曲图像重建每一层。这种基于学习的层重建模块有助于适应流量估计和脆弱假设中的潜在错误，例如亮度一致性。我们表明，所提出的方法从综合生成的数据中学习到的真实图像表现良好。实验结果表明，许多具有挑战性的场景反射和栅栏去除所提出的方法的有效性。
英文摘要：
We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera. Our method leverages motion differences between the background and obstructing elements to recover both layers. Specifically, we alternate between estimating dense optical flow fields of the two layers and reconstructing each layer from the flow-warped images via a deep convolutional neural network. This learning-based layer reconstruction module facilitates accommodating potential errors in the flow estimation and brittle assumptions, such as brightness consistency. We show that the proposed approach learned from synthetically generated data performs well to real images. Experimental results on numerous challenging scenarios of reflection and fence removal demonstrate the effectiveness of the proposed method.
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