  Image of a scene captured through a piece of transparent and reflective
material, such as glass, is often spoiled by a superimposed layer of reflection
image. While separating the reflection from a familiar object in an image is
mentally not difficult for humans, it is a challenging, ill-posed problem in
computer vision. In this paper, we propose a novel deep convolutional
encoder-decoder method to remove the objectionable reflection by learning a map
between image pairs with and without reflection. For training the neural
network, we model the physical formation of reflections in images and
synthesize a large number of photo-realistic reflection-tainted images from
reflection-free images collected online. Extensive experimental results show
that, although the neural network learns only from synthetic data, the proposed
method is effective on real-world images, and it significantly outperforms the
other tested state-of-the-art techniques.
