Improved optimization methods for image registration problems

Ke Chen; Geovani Nunes Grapiglia; Jinyun Yuan; Daoping Zhang

doi:10.1007/s11075-018-0486-2

Improved optimization methods for image registration problems

Ke Chen^*, Geovani Nunes Grapiglia, Jinyun Yuan, Daoping Zhang

^*Corresponding author for this work

Mathematics And Statistics

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

14 Downloads (Pure)

Abstract

In this paper, we propose new multilevel optimization methods for minimizing continuously differentiable functions obtained by discretizing models for image registration problems. These multilevel schemes rely on a novel two-step Gauss-Newton method, in which a second step is computed within each iteration by minimizing a quadratic approximation of the objective function over a certain two-dimensional subspace. Numerical results on image registration problems show that the proposed methods can outperform the standard multilevel Gauss-Newton method.

Original language	English
Pages (from-to)	305-336
Number of pages	32
Journal	Numerical Algorithms
Volume	80
Issue number	2
DOIs	https://doi.org/10.1007/s11075-018-0486-2
Publication status	Published - 6 Feb 2019

Funding

Funding information This work was partially supported by UK EPSRC (grants EP/K036939/1 and EP/N014499/1), by the Newton Research Collaboration Programme (grant NRCP 1617/6/187) and by the National Council for Scientific and Technological Development (grant CNPq 406269/2016-5).

Keywords

Gauss-Newton method
image registration
multilevel strategy
subspace methods

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10.1007/s11075-018-0486-2

Chen-etal-NA-2019-Improved-optimization-methods-for-image-registration-problemsAccepted author manuscript, 10.2 MBLicence: Strath_1

Cite this

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abstract = "In this paper, we propose new multilevel optimization methods for minimizing continuously differentiable functions obtained by discretizing models for image registration problems. These multilevel schemes rely on a novel two-step Gauss-Newton method, in which a second step is computed within each iteration by minimizing a quadratic approximation of the objective function over a certain two-dimensional subspace. Numerical results on image registration problems show that the proposed methods can outperform the standard multilevel Gauss-Newton method.",

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AU - Chen, Ke

AU - Grapiglia, Geovani Nunes

AU - Yuan, Jinyun

AU - Zhang, Daoping

PY - 2019/2/6

Y1 - 2019/2/6

N2 - In this paper, we propose new multilevel optimization methods for minimizing continuously differentiable functions obtained by discretizing models for image registration problems. These multilevel schemes rely on a novel two-step Gauss-Newton method, in which a second step is computed within each iteration by minimizing a quadratic approximation of the objective function over a certain two-dimensional subspace. Numerical results on image registration problems show that the proposed methods can outperform the standard multilevel Gauss-Newton method.

AB - In this paper, we propose new multilevel optimization methods for minimizing continuously differentiable functions obtained by discretizing models for image registration problems. These multilevel schemes rely on a novel two-step Gauss-Newton method, in which a second step is computed within each iteration by minimizing a quadratic approximation of the objective function over a certain two-dimensional subspace. Numerical results on image registration problems show that the proposed methods can outperform the standard multilevel Gauss-Newton method.

KW - Gauss-Newton method

KW - image registration

KW - multilevel strategy

KW - subspace methods

UR - https://livrepository.liverpool.ac.uk/3018792/

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Improved optimization methods for image registration problems

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