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Proceedings Paper

Self-learning Bayesian centroid estimation
Author(s): Nick Dillon; Charles R. Jenkins
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Paper Abstract

CCD read noise is the single most important factor which determines the performance of the Shack-Hartmann wavefront sensor in photon-starved applications. We address the problem of making optimum centroid estimates in sensors employing NxN-pixel centroiding geometries, where N is generally > 2 so that read noise is even more important than in quad-cell- based sensors. Maximum-likelihood and Bayesian estimators are derived and we show that these afford excellent noise suppression whilst relaxing the constraints on alignment tolerances and static aberrations which are demanded in quad-cell applications. The estimators considered are all linear and are shown to be implementable using conventional real-time processing hardware.

Paper Details

Date Published: 17 October 1997
PDF: 12 pages
Proc. SPIE 3126, Adaptive Optics and Applications, (17 October 1997); doi: 10.1117/12.290168
Show Author Affiliations
Nick Dillon, Royal Greenwich Observatory (United Kingdom)
Charles R. Jenkins, Royal Greenwich Observatory (United Kingdom)

Published in SPIE Proceedings Vol. 3126:
Adaptive Optics and Applications
Robert K. Tyson; Robert Q. Fugate, Editor(s)

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