Reflection removal using ghosting cues
Name
Reflection removal using ghosting cues.pdf
Size
9.32 MB
Format
Adobe PDF
Checksum (MD5)
7a3772152d41b9ce685ee84b156ccf77
Author(s)
Krishnan, Dilip
Shih, YiChang
Durand, Frederic
Freeman, William T.
Date Issued
October 2015
Journal
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
YiChang Shih, et al. "Reflection Removal Using Ghosting Cues." 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7-12 June 2015, Boston, Massachusetts, IEEE, 2015, pp. 3193–201.
Version
Author's final manuscript
Abstract
Photographs taken through glass windows often contain both the desired scene and undesired reflections. Separating the reflection and transmission layers is an important but ill-posed problem that has both aesthetic and practical applications. In this work, we introduce the use of ghosting cues that exploit asymmetry between the layers, thereby helping to reduce the ill-posedness of the problem. These cues arise from shifted double reflections of the reflected scene off the glass surface. In double-pane windows, each pane reflects shifted and attenuated versions of objects on the same side of the glass as the camera. For single-pane windows, ghosting cues arise from shifted reflections on the two surfaces of the glass pane. Even though the ghosting is sometimes barely perceptible by humans, we can still exploit the cue for layer separation. In this work, we model the ghosted reflection using a double-impulse convolution kernel, and automatically estimate the spatial separation and relative attenuation of the ghosted reflection components. To separate the layers, we propose an algorithm that uses a Gaussian Mixture Model for regularization. Our method is automatic and requires only a single input image. We demonstrate that our approach removes a large fraction of reflections on both synthetic and real-world inputs.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR.2015.7298939
