Paper
25 August 2005 Prediction of the space-varying point spread function for reconstruction of anisoplanatic adaptive optics images
Author Affiliations +
Abstract
Atmospheric turbulence corrupts astronomical images formed by ground-based telescopes. Adaptive optics (AO) systems allow the effects of turbulence-induced aberrations to be reduced for a narrow field of view (FOV) corresponding approximately to the isoplanatic angle θ0. For field angles larger than θ0, the point spread function (PSF) gradually degrades as the field angle increases. In this paper, we present a technique to predict the PSF as function of the field angle. The predicted PSF is compared to the simulated PSF and the mean square (MS) error between the predicted and the simulated PSF never exceeds 2.7%. Simulated anisoplanatic intensity images of a star field are reconstructed by mean of a block-processing method using the predicted PSF. Two methods for image recovery are used: the Tikhonov regularization and the expectation maximization (EM) algorithm. The deconvolution results using the space-varying predicted PSF are compared to deconvolution results using the space-invariant on-axis PSF. The reconstruction technique using the predicted PSF shows an improvement of the MS error between the reconstructed image and the object of 7.2% to 84.8% compared to the on-axis PSF reconstruction.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mathieu Aubailly, Michael C. Roggemann, and Timothy J. Schulz "Prediction of the space-varying point spread function for reconstruction of anisoplanatic adaptive optics images", Proc. SPIE 5903, Astronomical Adaptive Optics Systems and Applications II, 590308 (25 August 2005); https://doi.org/10.1117/12.639428
Lens.org Logo
CITATIONS
Cited by 5 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Point spread functions

Expectation maximization algorithms

Signal to noise ratio

Adaptive optics

Reconstruction algorithms

Deconvolution

Image restoration

Back to Top