  Traditional reflection removal algorithms either use a single image as input,
which suffers from intrinsic ambiguities, or use multiple images from a moving
camera, which is inconvenient for users. We instead propose a learning-based
dereflection algorithm that uses stereo images as input. This is an effective
trade-off between the two extremes: the parallax between two views provides
cues to remove reflections, and two views are easy to capture due to the
adoption of stereo cameras in smartphones. Our model consists of a
learning-based reflection-invariant flow model for dual-view registration, and
a learned synthesis model for combining aligned image pairs. Because no dataset
for dual-view reflection removal exists, we render a synthetic dataset of
dual-views with and without reflections for use in training. Our evaluation on
an additional real-world dataset of stereo pairs shows that our algorithm
outperforms existing single-image and multi-image dereflection approaches.
