A Study of Methods of Choosing the Smoothing Parameter in Image Restoration by Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The authors address the problem of power spectral density estimation of time series with auto-regressive (AR) models when only a short span of data is available for analysis. The AR coefficients are estimated through a regularized method proposed by G. Kitagawa and W. Gersch (1985). An experimental study of this method and a comparison with the classical least squares (LS) method are outlined. The principles of the statistical study and computation results are presented.