Abstract

Until recently moving objects could only be photographed with short exposure times, to avoid motion blur. Yet, recently two groundbreaking works in computational photography offer new camera designs allowing arbitrary exposure times. The “flutter shutter” of Agrawal et al. creates an invertible motion blur by using a clever shutter technique to interrupt the photon flux during the exposure time according to a well chosen binary sequence. The “motion-invariant photography” of Levin et al. gets the same result by a uniformly accelerated camera motion. This talk proposes a simple mathematical method for evaluating the image quality of these new cameras. The theory, providing explicit formulas for the SNR obtained after deconvolution, raises a central paradox for these cameras: It shows that even an infinite exposure time cannot bring an SNR increase of more than 17%! Nevertheless, three consequences of the theory permit to mitigate this harsh limitation. First, this SNR gain can be obtained on any video with moderate motion blur by a very simple new video temporal filter. The video improvement by this blind deconvolution is visible. Second, we show that if a probabilistic motion model is available, then one can compute an optimal flutter shutter with SNR exceeding significantly the predicted limit. Third, we show that the “best snapshot’’ for a given exposure time is not obtained with a constant aperture: it is obtained with a flutter shutter.

This is joint work with Yohann Tendero