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Reflections are very common phenomena in our daily photography, whichdistract people's attention from the scene behind the glass. The problem ofremoving reflection artifacts is important but challenging due to its ill-posednature. The traditional approaches solve an optimization problem over theconstraints induced from multiple images, at the expense of large computationcosts. Recent learning-based approaches have demonstrated a significantimprovement in both performance and running time for single image reflectionremoval, but are limited as they require a large number of syntheticreflection/clean image pairs for direct supervision to approximate the groundtruth, at the risk of overfitting in the synthetic image domain and degradingin the real image domain. In this paper, we propose a novel learning-basedsolution that combines the advantages of the aforementioned approaches andovercomes their drawbacks. Our algorithm works by learning a deep neuralnetwork to optimize the target with joint constraints enhanced among multipleinput images during the training phase, but is able to eliminate reflectionsonly from a single input for evaluation. Our algorithm runs in real-time andachieves state-of-the-art reflection removal performance on real images. Wefurther propose a strong network backbone that disentangles the background andreflection information into separate latent codes, which are embedded into ashared one-branch deep neural network for both background and reflectionpredictions. The proposed backbone experimentally performs better than theother common network implementations, and provides insightful knowledge tounderstand the reflection removal task.

Single image reflection removal via learning with multi-image constraints