  Removing the undesired reflections from images taken through the glass is of
broad application to various computer vision tasks. Non-learning based methods
utilize different handcrafted priors such as the separable sparse gradients
caused by different levels of blurs, which often fail due to their limited
description capability to the properties of real-world reflections. In this
paper, we propose the Concurrent Reflection Removal Network (CRRN) to tackle
this problem in a unified framework. Our proposed network integrates image
appearance information and multi-scale gradient information with human
perception inspired loss function, and is trained on a new dataset with 3250
reflection images taken under diverse real-world scenes. Extensive experiments
on a public benchmark dataset show that the proposed method performs favorably
against state-of-the-art methods.
