2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
Image -- Reflection -- Photography
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.
Copyright (c) 2021 The Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Locate the Document
Niklaus, S., Zhang, X. C., Barron, J. T., Wadhwa, N., Garg, R., Liu, F., & Xue, T. (2021). Learned Dual-View Reflection Removal. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/wacv48630.2021.00376