Share Email Print
cover

Proceedings Paper

Blur identification in super-resolution restoration with Arnoldi process
Author(s): Kai Xie; Yeli Li; Tong Li
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

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.

Paper Details

Date Published: 14 November 2007
PDF: 5 pages
Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 67890X (14 November 2007); doi: 10.1117/12.749713
Show Author Affiliations
Kai Xie, Beijing Institute of Graphic Communication (China)
Yeli Li, Beijing Institute of Graphic Communication (China)
Tong Li, Beijing Institute of Graphic Communication (China)


Published in SPIE Proceedings Vol. 6789:
MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques

© SPIE. Terms of Use
Back to Top