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5 March 2008 Adaptive optics images joint deconvolution based on power spectra density of object and PSF
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Proceedings Volume 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing; 66230J (2008) https://doi.org/10.1117/12.791404
Event: International Symposium on Photoelectronic Detection and Imaging: Technology and Applications 2007, 2007, Beijing, China
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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bo Chen, Ze-xun Geng, Mao-lin Wang, Jing Yang, and Yax-in Zhang "Adaptive optics images joint deconvolution based on power spectra density of object and PSF", Proc. SPIE 6623, International Symposium on Photoelectronic Detection and Imaging 2007: Image Processing, 66230J (5 March 2008); https://doi.org/10.1117/12.791404
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