The ability to reliably amplify subtle motions in a video is a wonderful tool for investigating a wide range of phenomena we see in the natural world. Such techniques enable us to visualize the subtle blood flow in a person's face, the rise and fall of a sleeping infant's chest, the vibrations of a bridge swaying in the wind, and even the almost imperceptible trembling of leaves due to musical notes.
The development of image processing techniques to amplify such small motions is one of the recent breakthroughs in the computational photography field, which applies algorithmic enhancement techniques to photos and videos in order to create images that could not be captured with regular photography. Some of the earlier work on this topic (originating from the same research group at MIT) used motion estimation (optical flow) techniques to recover small motions, amplify them, and then digitally warp the images. Unfortunately, optical flow techniques are very sensitive to noise, lack of texture, and discontinuities, which make this approach very brittle.
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