  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 if a region is
reflection-dominated or transmission-dominated, and it is used as a cue for the
network to control the feature flow when predicting the reflection and
transmission layers. We design our network as a recurrent network to
progressively refine reflection removal results at each iteration. The novelty
is that we leverage Laplacian kernel parameters to emphasize the boundaries of
strong reflections. It is beneficial to strong reflection detection and
substantially improves the quality of reflection removal results. Extensive
experiments verify the superior performance of the proposed method over
state-of-the-art approaches. Our code and the pre-trained model can be found at
https://github.com/zdlarr/Location-aware-SIRR.
