  We address for the first time the issue of motion blur in light field images
captured from plenoptic cameras. We propose a solution to the estimation of a
sharp high resolution scene radiance given a blurry light field image, when the
motion blur point spread function is unknown, i.e., the so-called blind
deconvolution problem. In a plenoptic camera, the spatial sampling in each view
is not only decimated but also defocused. Consequently, current blind
deconvolution approaches for traditional cameras are not applicable. Due to the
complexity of the imaging model, we investigate first the case of uniform
(shift-invariant) blur of Lambertian objects, i.e., when objects are
sufficiently far away from the camera to be approximately invariant to depth
changes and their reflectance does not vary with the viewing direction. We
introduce a highly parallelizable model for light field motion blur that is
computationally and memory efficient. We then adapt a regularized blind
deconvolution approach to our model and demonstrate its performance on both
synthetic and real light field data. Our method handles practical issues in
real cameras such as radial distortion correction and alignment within an
energy minimization framework.
