Blur identification in super-resolution restoration with Arnoldi process
Kai Xie, Yeli Li, Tong Li · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
The point spread function (PSF) parameters of the imaging system are not often known a prior in super-resolution enhancement applications. In our super-resolution algorithm, we identify the PSF and regularization parameters from the raw data using the generalized cross-validation method (GCV). Motivated by the success of GCV in identifying optimal smoothing parameters for image restoration, we have extended the method to the problem of estimating blur parameters. To reduce the computational complexity of GCV, we propose efficient approximation techniques based on the Arnoldi process. The Arnoldi process can yield a small and condensed Hessenberg matrix which is orthogonal bases of the Krylov subspaces. Experiments are presented which demonstrate the effectiveness and robustness of our method.