A novel GCV-based criterion for parametric PSF estimation

We propose a generalized cross validation (GCV) as a novel criterion for estimating a 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 PSF, we then perform deconvolution using our recently developed SURE-LET algorithm. The GCV-based criterion 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 the GCV minimization yields highly accurate estimates of the PSF parameters, which also result in a negligible loss of visual quality, compared to that obtained with the exact PSF. The highly competitive results outline the great potential of developing more powerful blind deconvolution algorithms based on this criterion.