This paper addresses the problem of restoring images subjected to unknown and
spatially varying blur caused by defocus or linear (say, horizontal) motion.
The estimation of the global (non-uniform) image blur is cast as a multi-label
energy minimization problem. The energy is the sum of unary terms corresponding
to learned local blur estimators, and binary ones corresponding to blur
smoothness. Its global minimum is found using Ishikawa's method by exploiting
the natural order of discretized blur values for linear motions and defocus.
Once the blur has been estimated, the image is restored using a robust
(non-uniform) deblurring algorithm based on sparse regularization with global
image statistics. The proposed algorithm outputs both a segmentation of the
image into uniform-blur layers and an estimate of the corresponding sharp
image. We present qualitative results on real images, and use synthetic data to
quantitatively compare our approach to the publicly available implementation of
Chakrabarti et al. (2010).