Abstract

Motion blurs confound many computer vision problems. The fluttered shutter (FS) camera [1] tackles the motion deblurring problem by emulating invertible broadband blur kernels. However, existing FS methods assume known constant velocity motions, e.g., via user specifi- cations. In this paper, we extend the FS technique to general 1D motions and develop an automatic motion-from-blur framework by analyzing the image statistics under the FS. We first introduce a fluttered-shutter point-spread-function (FS-PSF) to uniformly model the blur kernel under general motions. We show that many commonly used motions have closed-form FS-PSFs. To recover the FS-PSF from the blurred image, we present a new method by an- alyzing image power spectrum statistics. We show that the Modulation Transfer Function of the 1D FS-PSF is statistically correlated to the blurred image power spectrum along the motion direction. We then re- cover the FS-PSF by finding the motion parameters that maximize the correlation. We demonstrate our techniques on a variety of motions in- cluding constant velocity, constant acceleration, and harmonic rotation. Experimental results show that our method can automatically and ac- curately recover the motion from the blurs captured under the fluttered shutter.