Blur identification in super-resolution restoration with Arnoldi process

Kai Xie; Yeli Li; Tong Li

doi:10.1117/12.749713

14 November 2007 Blur identification in super-resolution restoration with Arnoldi process

Kai Xie, Yeli Li, Tong Li

Proceedings Volume 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 67890X (2007) https://doi.org/10.1117/12.749713
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Kai Xie, Yeli Li, and Tong Li "Blur identification in super-resolution restoration with Arnoldi process", Proc. SPIE 6789, MIPPR 2007: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 67890X (14 November 2007); https://doi.org/10.1117/12.749713

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