Multi-view Separation of Background and Reflection by Coupled Low-Rank Decomposition

Images captured by a camera through glass often have reflection superimposed on the transmitted background. Among existing methods for reflection separation, multi-view methods are the most convenient to apply because they require the user to just take multiple images of a scene at varying viewing angles. Some of these methods are restricted to the simple case where the background scene and reflection scene are planar. The methods that handle non-planar scenes employ image feature flow to capture correspondence for image alignment, but they can overfit resulting in degraded performance. This paper proposes a multiple-view method for separating background and reflection based on robust principal component analysis. It models the background and reflection as rank-1 matrices, which are decomposed according to different transformations for aligning the background and reflection images. It can handle non-planar scenes and global reflection. Comprehensive test results show that our method is more accurate and robust than recent related methods.