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Proceedings Paper

Adaptive optics images joint deconvolution based on power spectra density of object and PSF
Author(s): Bo Chen; Ze-xun Geng; Mao-lin Wang; Jing Yang; Yax-in Zhang
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Paper Abstract

The atmospheric turbulence severely limits the angular resolution of ground based telescopes. When using Adaptive Optics (AO) compensation, the wavefront sensor data permit the estimation of the residual PSF. Yet, this estimation is imperfect, and a deconvolution is required for reaching the diffraction limit. A joint deconvolution method based on power spectra density (PSD) for AO image is presented. It deduces from a Bayesian framework in the context of imaging through turbulence with adaptive optics. This method uses a noise model that accounts for photonic and detector noises. It incorporates a positivity constraint and some a priori knowledge of the object (an estimate of its local mean and a model for its power spectral density). Finally, it reckons with an imperfect knowledge of the point spread function (PSF) by estimating the PSF jointly with the object under soft constraints rather than blindly. These constraints are designed to embody our knowledge of the PSF. Deconvolution results are presented for both simulated and experimental data.

Paper Details

Date Published: 5 March 2008
PDF: 12 pages
Proc. SPIE 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing, 66230J (5 March 2008); doi: 10.1117/12.791404
Show Author Affiliations
Bo Chen, Information Engineering Univ. (China)
Guilin Air Force Academy (China)
Ze-xun Geng, Information Engineering Univ. (China)
Mao-lin Wang, Guilin Air Force Academy (China)
Jing Yang, Guilin Air Force Academy (China)
Yax-in Zhang, Information Engineering Univ. (China)

Published in SPIE Proceedings Vol. 6623:
International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing
Liwei Zhou, Editor(s)

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