title: A novel SURE-based criterion for parametric PSF estimation.
abstract: We propose an unbiased estimate of a filtered version of the mean squared error--the blur-SURE (Stein's unbiased risk estimate)--as a novel criterion for estimating an unknown point spread function (PSF) from the degraded image only. The PSF is obtained by minimizing this new objective functional over a family of Wiener processings. Based on this estimated blur kernel, we then perform nonblind deconvolution using our recently developed algorithm. The SURE-based framework is exemplified with a number of parametric PSF, involving a scaling factor that controls the blur size. A typical example of such parametrization is the Gaussian kernel. The experimental results demonstrate that minimizing the blur-SURE yields highly accurate estimates of the PSF parameters, which also result in a restoration quality that is very similar to the one obtained with the exact PSF, when plugged into our recent multi-Wiener SURE-LET deconvolution algorithm. The highly competitive results obtained outline the great potential of developing more powerful blind deconvolution algorithms based on SURE-like estimates.
authors.forename: Feng
authors.initials: F
authors.lastname: Xue
authors.forename: Thierry
authors.initials: T
authors.lastname: Blu
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical