  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.
