Computer Science – Performance
Scientific paper
Oct 1996
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1996spie.2828..332c&link_type=abstract
Proc. SPIE Vol. 2828, p. 332-339, Image Propagation through the Atmosphere, J. C. Dainty; Luc R. Bissonnette; Eds.
Computer Science
Performance
1
Scientific paper
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.
Conan Jean-Marc
Michau Vincent
Rousset Gerard
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