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Blind restoration/superresolution with generalized cross-validation using Gauss-type quadrature rules

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3 Author(s)
Nguyen, N. ; SCCM Program, Stanford Univ., CA, USA ; Golub, G. ; Milanfar, P.

In many restoration/superresolution applications, the blurring process, i.e., point spread function (PSF) of the imaging system, is not known. We estimate the PSF and regularization parameters for this ill-posed inverse problem from raw data using the generalized cross-validation method (GCV). To reduce the computational complexity of GCV we propose efficient approximation techniques based on the Lanczos algorithm and Gauss quadrature theory. Data-driven blind restoration/superresolution experiments are presented to demonstrate the effectiveness and robustness of our method.

Published in:

Signals, Systems, and Computers, 1999. Conference Record of the Thirty-Third Asilomar Conference on  (Volume:2 )

Date of Conference:

24-27 Oct. 1999

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