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Fast regularized structured total least squares algorithm for solving the basic deconvolution problem
Author(s) -
Mastronardi N.,
Lemmerling P.,
Van Huffel S.
Publication year - 2004
Publication title -
numerical linear algebra with applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.02
H-Index - 53
eISSN - 1099-1506
pISSN - 1070-5325
DOI - 10.1002/nla.413
Subject(s) - deconvolution , mathematics , algorithm , regularization (linguistics) , estimator , least squares function approximation , mathematical optimization , total least squares , non linear least squares , blind deconvolution , computer science , estimation theory , statistics , artificial intelligence , singular value decomposition
In this paper, we develop a fast regularized structured total least squares (RSTLS) algorithm for solving the basic deconvolution problem. The algorithm is based on a particular implementation of the generalized Schur algorithm. We apply the new algorithm on a deconvolution problem arising in a medical application in renography. By means of this example, we show that regularization in the structured total least squares framework yields more accurate results compared to other estimators. Copyright © 2004 John Wiley & Sons, Ltd.