Share Email Print
cover

Proceedings Paper

Krylov subspace iterative methods for nonsymmetric discrete ill-posed problems in image restoration
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The BiCG and QMR methods are well-known Krylov subspace iterative methods for the solution of linear systems of equations with a large nonsymmetric, nonsingular matrix. However, little is known of the performance of these methods when they are applied to the computation of approximate solutions of linear systems of equations with a matrix of ill-determined rank. Such linear systems are known as linear discrete ill-posed problems. We describe an application of the BiCG and QMR methods to the solution of linear discrete ill-posed problems that arise in image restoration, and compare these methods to the conjugate gradient method applied to the associated normal equations and to total variation-penalized Tikhonov regularization.

Paper Details

Date Published: 20 November 2001
PDF: 10 pages
Proc. SPIE 4474, Advanced Signal Processing Algorithms, Architectures, and Implementations XI, (20 November 2001); doi: 10.1117/12.448653
Show Author Affiliations
Daniela Calvetti, Case Western Reserve Univ. (United States)
Bryan Lewis, Rocketcalc, LLC (United States)
Lothar Reichel, Kent State Univ. (United States)


Published in SPIE Proceedings Vol. 4474:
Advanced Signal Processing Algorithms, Architectures, and Implementations XI
Franklin T. Luk, Editor(s)

© SPIE. Terms of Use
Back to Top