Location-aware Single Image Reflection Removal

This paper proposes a novel location-aware deep learning-based single image reflection removal method. Our network has a reflection detection module to regress a probabilistic reflection confidence map, taking multi-scale Laplacian features as inputs. This probabilistic map tells whether a region is reflection-dominated or transmission-dominated. The novelty is that we use the reflection confidence map as the cues for the network to learn how to encode the reflection information adaptively and control the feature flow when predicting reflection and transmission layers. The integration of location information into the network significantly improves the quality of reflection removal results. Besides, a set of learnable Laplacian kernel parameters is introduced to facilitate the extraction of discriminative Laplacian features for reflection detection. We design our network as a recurrent network to progressively refine each iteration's reflection removal results. Extensive experiments verify the superior performance of the proposed method over state-of-the-art approaches.