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Proceedings Paper

Performance of optimal registration estimators
Author(s): Tuan Quang Pham; Marijn Bezuijen; Lucas J. van Vliet; Klamer Schutte; Cris L. Luengo Hendriks
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Paper Abstract

This paper derives a theoretical limit for image registration and presents an iterative estimator that achieves the limit. The variance of any parametric registration is bounded by the Cramer-Rao bound (CRB). This bound is signal-dependent and is proportional to the variance of input noise. Since most available registration techniques are biased, they are not optimal. The bias, however, can be reduced to practically zero by an iterative gradient-based estimator. In the proximity of a solution, this estimator converges to the CRB with a quadratic rate. Images can be brought close to each other, thus speedup the registration process, by a coarse-to-tne multi-scale registration. The performance of iterative registration is finally shown to significantly increase image resolution from multiple low resolution images under translational motions.

Paper Details

Date Published: 25 May 2005
PDF: 12 pages
Proc. SPIE 5817, Visual Information Processing XIV, (25 May 2005); doi: 10.1117/12.603304
Show Author Affiliations
Tuan Quang Pham, Delft Univ. of Technology (Netherlands)
Marijn Bezuijen, Delft Univ. of Technology (Netherlands)
Lucas J. van Vliet, Delft Univ. of Technology (Netherlands)
Klamer Schutte, TNO Physics and Electronics Lab. (Netherlands)
Cris L. Luengo Hendriks, Lawrence Berkeley National Lab. (United States)


Published in SPIE Proceedings Vol. 5817:
Visual Information Processing XIV
Zia-ur Rahman; Robert A. Schowengerdt; Stephen E. Reichenbach, Editor(s)

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