Motion Denoising with Application to Time-lapse Photography

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 rerenders 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 shortterm 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.

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Michael Rubinstein, Ce Liu, Bill Freeman
• Keywords: Computer Vision, CVPR 2011, Short-term Changes, Time-lapse, Time-lapse Videos