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Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement

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3 Author(s)
Nguyen, N. ; Sci. Comput. & Comput. Math Program, Stanford Univ., CA, USA ; Milanfar, P. ; Golub, G.

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

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Image Processing, IEEE Transactions on  (Volume:10 ,  Issue: 9 )