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.
@article{arxiv.2010.00702,
title = {Learned Dual-View Reflection Removal},
author = {Simon Niklaus and Xuaner Cecilia Zhang and Jonathan T. Barron and Neal Wadhwa and Rahul Garg and Feng Liu and Tianfan Xue},
journal= {arXiv preprint arXiv:2010.00702},
year = {2020}
}