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Proceedings Paper

Quasi-Newton methods for image restoration
Author(s): James G Nagy; Katrina Palmer
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Paper Abstract

Many iterative methods that are used to solve Ax=b can be derived as quasi-Newton methods for minimizing the quadratic function 1/2 xTATAx-xTATb. In this paper, several such methods are considered, including conjugate gradient least squares (CGLS), Barzilai-Borwein (BB), residual norm steepest descent (RNSD) and Landweber (LW). Regularization properties of these methods are studied by analyzing the so-called "filter factors". The algorithm proposed by Barzilai and Borwein is shown to have very favorable regularization and convergence properties. Secondly, we find that preconditioning can result in much better convergence properties for these iterative methods.

Paper Details

Date Published: 26 October 2004
PDF: 11 pages
Proc. SPIE 5559, Advanced Signal Processing Algorithms, Architectures, and Implementations XIV, (26 October 2004); doi: 10.1117/12.561060
Show Author Affiliations
James G Nagy, Emory Univ. (United States)
Katrina Palmer, Appalachian State Univ. (United States)


Published in SPIE Proceedings Vol. 5559:
Advanced Signal Processing Algorithms, Architectures, and Implementations XIV
Franklin T. Luk, Editor(s)

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