Robust Separation of Reflection from Multiple Images

Xiaojie Guo, Xiaochun Cao, Yi Ma; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 2187-2194

When one records a video/image sequence through a transparent medium (e.g. glass), the image is often a superposition of a transmitted layer (scene behind the medium) and a reflected layer. Recovering the two layers from such images seems to be a highly ill-posed problem since the number of unknowns to recover is twice as many as the given measurements. In this paper, we propose a robust method to separate these two layers from multiple images, which exploits the correlation of the transmitted layer across multiple images, and the sparsity and independence of the gradient fields of the two layers. A novel Augmented Lagrangian Multiplier based algorithm is designed to efficiently and effectively solve the decomposition problem. The experimental results on both simulated and real data demonstrate the superior performance of the proposed method over the state of the arts, in terms of accuracy and simplicity.