Unformatted text preview: algorithms reduce this noise. • In many applications, we need to choose the regularization parameter by automatic methods rather than by eye. If the noiselevel is known, then the discrepancy principle is the best: choose the parameter to make the residual Kf − g close in norm to the expected norm of the noise. If the noiselevel is not known, then generalized cross validation and the Lcurve are popular methods. See [1,2] for discussion of such methods. [1] Per Christian Hansen, James M. Nagy, and Dianne P. O’Leary. Deblurring Images: Matrices, Spectra, and Filtering . SIAM Press, Philadelphia, 2006. [2] Bert W. Rust and Dianne P. O’Leary, “Residual Periodograms for Choosing Regularization Parameters for IllPosed Problems”, Inverse Problems, 24 (2008) 034005....
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 Fall '11
 Dr.Robin
 Inverse problem, regularization parameter, Dianne P. O’Leary

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