Share Email Print
cover

Proceedings Paper

Regularized image registration with line search optimization
Author(s): Lin Gan; Gady Agam
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Image registration is normally solved as a regularized optimization problem. The line search procedure is commonly employed in unconstrained nonlinear optimization. At each iteration step the procedure computes a step size that achieves adequate reduction in the objective function at minimal cost.In this paper we extend the constrained line search procedure with different regularization terms so as to improve convergence. The extension is addressed in the context of constrained optimization to solve a regularized image registration problem. Specifically, the displacement field between the registered image pair is modeled as the sum of weighted Discrete Cosine Transform basis functions. A Taylor series expansion is applied to the objective function for deriving a Gauss-Newton solution. We consider two regularization terms added to the objective function. A Tikhonov regularization term constrains the magnitude of the solution and a bending energy term constrains the bending energy of the deformation field. We modify both the sufficient and curvature conditions of the Wolfe conditions to accommodate the additional regularization terms. The proposed extension is evaluated by generated test collection with known deformation. The experimental evaluation results show that a solution obtained with bending energy regularization and Wolfe condition line search achieves the smallest mean deformation field error among 100 registration pairs. This solution shows in addition an improvement in overcoming local minima.

Paper Details

Date Published: 12 March 2015
PDF: 9 pages
Proc. SPIE 9401, Computational Imaging XIII, 94010E (12 March 2015); doi: 10.1117/12.2083424
Show Author Affiliations
Lin Gan, Illinois Institute of Technology (United States)
Gady Agam, Illinois Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9401:
Computational Imaging XIII
Charles A. Bouman; Ken D. Sauer, Editor(s)

© SPIE. Terms of Use
Back to Top