Images captured behind glass are often contaminated by the undesired reflection that hinders other computer vision tasks. Due to ubiquitous glasses, the removal of undesired reflection becomes more important. Reflection is determined by imaging system parameters such as the transparency, color of the glasses, the position of the camera. In this letter, we propose a novel generative model based on a generative adversarial network (GAN), where an Encoder extracts system parameters from input images and encodes them succinctly as a distribution and a Generator emulates the optical process of reflection. Separating the optical process of the system parameters allows our model to cope with diversified real-life scenarios and avoid the mode collapse phenomenon. Based on the generative model, we introduce a reflection removal model that simultaneously extracts the original image, the reflection layer, and the encoded parameters from single image input. Computational results of real data show that our approach outperforms existing approaches.

中文翻译：

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将成像系统参数编码为分布以改善反射生成和消除

在玻璃后面拍摄的图像通常会受到阻碍其他计算机视觉任务的不希望的反射的污染。由于无处不在的眼镜，去除不需要的反射变得更加重要。反射由成像系统参数决定，例如透明度、眼镜的颜色、相机的位置。在这封信中，我们提出了一种基于生成对抗网络 (GAN) 的新型生成模型，其中编码器从输入图像中提取系统参数并将它们简洁地编码为分布，生成器模拟反射的光学过程。分离系统参数的光学过程使我们的模型能够应对多样化的现实生活场景并避免模式崩溃现象。基于生成模型，我们引入了一个反射去除模型，它同时从单个图像输入中提取原始图像、反射层和编码参数。真实数据的计算结果表明，我们的方法优于现有方法。