Abstract
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-ofthe-art methods