title: Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement.
abstract: In many image restoration/resolution enhancement applications, the blurring process, i.e., point spread function (PSF) of the imaging system, is not known or is known only to within a set of parameters. We estimate these PSF parameters for this ill-posed class of inverse problem from raw data, along with the regularization parameters required to stabilize the solution, using the generalized cross-validation method (GCV). We propose efficient approximation techniques based on the Lanczos algorithm and Gauss quadrature theory, reducing the computational complexity of the GCV. Data-driven PSF and regularization parameter estimation experiments with synthetic and real image sequences are presented to demonstrate the effectiveness and robustness of our method.
authors.affiliation: Sci. Comput. and Comput. Math Program, Stanford Univ., CA 94305-9025, USA. nguyen@sccm.stanford.edu
authors.forename: N
authors.initials: N
authors.lastname: Nguyen
authors.forename: P
authors.initials: P
authors.lastname: Milanfar
authors.forename: G
authors.initials: G
authors.lastname: Golub