Motion denoising with application to time-lapse photography
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Durand_Motion denoising.pdf
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3.04 MB
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Author(s)
Rubinstein, Michael
Liu, Ce
Sand, Peter
Durand, Fredo
Freeman, William T.
Date Issued
June 2011
Journal
Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Rubinstein, Michael, Ce Liu, Peter Sand, Fredo Durand, and William T. Freeman. “Motion Denoising with Application to Time-Lapse Photography.” CVPR 2011 (n.d.).
Version
Author's final manuscript
Abstract
Motions can occur over both short and long time scales. We introduce motion denoising, which treats short-term changes as noise, long-term changes as signal, and re-renders a video to reveal the underlying long-term events. We demonstrate motion denoising for time-lapse videos. One of the characteristics of traditional time-lapse imagery is stylized jerkiness, where short-term changes in the scene appear as small and annoying jitters in the video, often obfuscating the underlying temporal events of interest. We apply motion denoising for resynthesizing time-lapse videos showing the long-term evolution of a scene with jerky short-term changes removed. We show that existing filtering approaches are often incapable of achieving this task, and present a novel computational approach to denoise motion without explicit motion analysis. We demonstrate promising experimental results on a set of challenging time-lapse sequences.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/CVPR.2011.5995374
