Abstract:
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 finit...View moreMetadata
Abstract:
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.
Date of Conference: 11-14 September 2011
Date Added to IEEE Xplore: 29 December 2011
ISBN Information: