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Proceedings Paper

Signal-to-noise ratio and bias of various deconvolution from wavefront sensing estimators
Author(s): Jean-Marc Conan; Vincent Michau; Gerard Rousset
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Paper Abstract

Deconvolution from wavefront sensing is a powerful and relatively low cost high resolution imaging technique compensating for the degradation due to atmospheric turbulence. It is based on a simultaneous recording of short exposure images and wavefront sensing data. Two different deconvolution schemes have been proposed: the self- referenced estimator originally presented by Primot et al. and the post-referenced estimator recently suggested by Roggemann et al. A theoretical study allows us to estimate the bias and signal to noise ratio of these various estimators. Self-referenced deconvolution is shown to have a good signal-to-noise ratio but the estimator is biased, while post-referenced deconvolution is bias-free but has very limited performance for bright sources. A new-self referenced deconvolution scheme accounting for the wavefront sensing noise is proposed. This leads to an optimal data reduction which should overcome the bias problems while providing good signal-to-noise ratio performances. Encouraging numerical results are presented.

Paper Details

Date Published: 14 October 1996
PDF: 8 pages
Proc. SPIE 2828, Image Propagation through the Atmosphere, (14 October 1996); doi: 10.1117/12.254206
Show Author Affiliations
Jean-Marc Conan, Office National d'Etudes et de Recherches Aerospatiales (France)
Vincent Michau, Office National d'Etudes et de Recherches Aerospatiales (France)
Gerard Rousset, Office National d'Etudes et de Recherches Aerospatiales (France)


Published in SPIE Proceedings Vol. 2828:
Image Propagation through the Atmosphere
Christopher Dainty; Luc R. Bissonnette, Editor(s)

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