When photographing through a piece of glass, reflections usually degrade the quality of captured images or videos. In this paper, by exploiting periodically varying light flickering, we investigate the problem of removing strong reflections from contaminated image sequences or videos with a unified capturing setup. We propose a learning-based method that utilizes short-term and long-term observations of mixture videos to exploit one-side contextual clues in fluctuant components and brightness-consistent clues in consistent components for achieving layer separation and flickering removal, respectively. A dataset containing synthetic and real mixture videos with light flickering is built for network training and testing. The effectiveness of the proposed method is demonstrated by the comprehensive evaluation on synthetic and real data, the application for video flickering removal, and the exploratory experiment on high-speed scenes.

透过玻璃拍摄时，反射通常会降低拍摄图像或视频的质量。在本文中，通过利用周期性变化的光闪烁，我们研究了使用统一的捕获设置从受污染的图像序列或视频中去除强反射的问题。我们提出了一种基于学习的方法，利用混合视频的短期和长期观察来利用波动成分中的单侧上下文线索和一致成分中的亮度一致线索，分别实现层分离和闪烁消除。为网络训练和测试构建了包含具有光闪烁的合成和真实混合视频的数据集。通过对合成数据和真实数据的综合评估、视频闪烁消除的应用以及高速场景的探索性实验，证明了该方法的有效性。