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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. OLeary. Deblurring Images: Matrices, Spectra, and Filtering . SIAM Press, Philadelphia, 2006. [2] Bert W. Rust and Dianne P. OLeary, Residual Periodograms for Choosing Regularization Parameters for IllPosed Problems, Inverse Problems, 24 (2008) 034005....
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 Fall '11
 Dr.Robin

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