Introduction - If you have any usage issues, please Google them yourself
We present a new approach for spatially varying blur identification
using a single image. Within each local patch in the
image, the local blur is selected between a finite set of candidate
PSFs by a maximum likelihood approach. We propose
to work with a Generalized Likelihood to reduce the number
of parameters and we use the Generalized Singular Value Decomposition
to limit the computing cost, while making proper
image boundary hypotheses. The resulting method is fast and
demonstrates good performance on simulated and real examples
originating from applications such as motion blur identification
and depth from defocus.