### Abstract

Original language | English |
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Title of host publication | Proceedings of WSC 2011 |

Publication date | 2011 |

Publication status | Published - 2011 |

Event | 36th Conference of the Dutch-Flemish Numerical Analysis Communities: Woudschouten - Zeist, Netherlands Duration: 5 Oct 2011 → 7 Oct 2011 Conference number: 36 |

### Conference

Conference | 36th Conference of the Dutch-Flemish Numerical Analysis Communities |
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Number | 36 |

Country | Netherlands |

City | Zeist |

Period | 05/10/2011 → 07/10/2011 |

### Cite this

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*Proceedings of WSC 2011.*36th Conference of the Dutch-Flemish Numerical Analysis Communities, Zeist, Netherlands, 05/10/2011.

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

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Image Deblurring with Krylov Subspace Methods

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.

M3 - Article in proceedings

BT - Proceedings of WSC 2011

ER -