Fragmented aperture imaging for motion and defocus deblurring

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.