Abstract
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.
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Lai, J., Leow, W.K., Sim, T., Li, G. (2017). Multi-view Separation of Background and Reflection by Coupled Low-Rank Decomposition. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_22
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DOI: https://doi.org/10.1007/978-3-319-64698-5_22
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