Blur identification by the method of generalized cross-validation

Abstract

The point spread function (PSF) of a blurred image is often unknown a priori; the blur must first be identified from the degraded image data before restoring the image. Generalized cross-validation (GCV) is introduced to address the blur identification problem. The GCV criterion identifies model parameters for the blur, the image, and the regularization parameter, providing all the information necessary to restore the image. Experiments are presented which show that GVC is capable of yielding good identification results. A comparison of the GCV criterion with maximum-likelihood (ML) estimation shows the GCV often outperforms ML in identifying the blur and image model parameters.