Coded Aperture Imaging


In this paper we present a space-varying deblurring algorithm from a single defocused and motion-blurred image obtained with a fragmented aperture. We show that, for the same overall incoming light, a fragmented aperture leads to better motion and defocus deblurring than a (compact) conventional circular aperture. We demonstrate that not only fragmented apertures preserve more spectrum of an image of the scene than traditional circular apertures, but they also allow a better identification of blur scale. Our algorithm estimates both motion blur magnitude and direction as well as defocus blur scale at each pixel. The estimation of the blur parameters is addressed by using local projections on subspaces and L1 regularization, while deblurring is posed as a variational minimization problem and solved via linearization of the Euler-Lagrange equations. The technique produces convincing results on real scenario.m that can be solved via graph cuts (without user interaction). Performance and results on both synthetic and real data are reported and compared with previous methods.

ICIP 2011

Fragmented Aperture Imaging
for Motion and Defocus Deblurring

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