Self-learning Bayesian centroid estimation

Nick Dillon; Charles R. Jenkins

doi:10.1117/12.290168

17 October 1997 Self-learning Bayesian centroid estimation

Nick Dillon, Charles R. Jenkins

Proceedings Volume 3126, Adaptive Optics and Applications; (1997) https://doi.org/10.1117/12.290168
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

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.

Citation Download Citation

Nick Dillon and Charles R. Jenkins "Self-learning Bayesian centroid estimation", Proc. SPIE 3126, Adaptive Optics and Applications, (17 October 1997); https://doi.org/10.1117/12.290168

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