Image Deblurring with Krylov Subspace Methods

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2011

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Image Deblurring with Krylov Subspace Methods. / Hansen, Per Christian (Invited author).

Proceedings of WSC 2011. 2011.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2011

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Hansen, PC 2011, 'Image Deblurring with Krylov Subspace Methods'. in Proceedings of WSC 2011.

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CBE

Hansen PC. 2011. Image Deblurring with Krylov Subspace Methods. In Proceedings of WSC 2011.

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Hansen PC. Image Deblurring with Krylov Subspace Methods. In Proceedings of WSC 2011. 2011.

Author

Hansen, Per Christian (Invited author) / Image Deblurring with Krylov Subspace Methods.

Proceedings of WSC 2011. 2011.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2011

Bibtex

@inbook{6dd58ba42ad84b959868d5bb343471ed,
title = "Image Deblurring with Krylov Subspace Methods",
author = "Hansen, {Per Christian}",
year = "2011",
booktitle = "Proceedings of WSC 2011",

}

RIS

TY - GEN

T1 - Image Deblurring with Krylov Subspace Methods

A1 - Hansen,Per Christian

AU - Hansen,Per Christian

PY - 2011

Y1 - 2011

N2 - Image deblurring, i.e., reconstruction of a sharper image from a blurred and noisy one, involves the solution of a large and very ill-conditioned system of linear equations, and regularization is needed in order to compute a stable solution. Krylov subspace methods are often ideally suited for this task: their iterative nature is a natural way to handle such large-scale problems, and the underlying Krylov subspace provides a convenient mechanism to regularized the problem by projecting it onto a low-dimensional "signal subspace" adapted to the particular problem. In this talk we consider the three Krylov subspace methods CGLS, MINRES, and GMRES. We describe their regularizing properties, and we discuss some computational aspects such as preconditioning and stopping criteria.

AB - Image deblurring, i.e., reconstruction of a sharper image from a blurred and noisy one, involves the solution of a large and very ill-conditioned system of linear equations, and regularization is needed in order to compute a stable solution. Krylov subspace methods are often ideally suited for this task: their iterative nature is a natural way to handle such large-scale problems, and the underlying Krylov subspace provides a convenient mechanism to regularized the problem by projecting it onto a low-dimensional "signal subspace" adapted to the particular problem. In this talk we consider the three Krylov subspace methods CGLS, MINRES, and GMRES. We describe their regularizing properties, and we discuss some computational aspects such as preconditioning and stopping criteria.

UR - http://wsc.project.cwi.nl/woudschoten/2011/conferentieE.php

BT - Proceedings of WSC 2011

T2 - Proceedings of WSC 2011

ER -