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Sparsity based reflection removal using external patch search

Reflection removal aims at separating the mixture of the desired background scenes and the undesired reflections, when the photos are taken through the glass. It has both aesthetic and practical applications which can largely improve the performance of many multimedia tasks. Existing reflection removal approaches heavily rely on scene priors such as separable sparse gradients brought by different levels of blur, and they easily fail when such priors are not observed in many real scenes. Sparse representation models and nonlocal image priors have shown their effectiveness in image restoration with self similarity. In this work, we propose a reflection removal method benefited from the sparsity and nonlocal image prior as a unified optimization framework. We leverage the retrieved image patch from an external database to overcome the limited prior information in the input mixture image and self similarity search. The experimental results show that our proposed model performs better than the existing state-of-the-art reflection removal method for both objective and subjective image qualities.