Prediction of the space-varying point spread function for reconstruction of anisoplanatic adaptive optics images

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 θ 0. For field angles larger than θ 0, 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