Abstract
Images measured through the atmosphere are degraded by scattering and absorption from aerosols along the path and by atmospheric turbulence. In the presence of heavy scattering at visible and infrared wavelengths, the distances over which reasonable observations are possible are quite short compared to astronomical imaging paradigms, and aerosol scattering effects dominate the degradation of the point spread function (PSF) due to atmospheric effects. In addition to aerosol-induced blurring, measurement noise effects are present in observed images. We examine the problem of reconstructing images degraded by aerosol blur and measurement noise using estimates of the overall PSF, which account for unscattered and scattered radiation detected by the imaging system. Representative images of a spoke target acquired under various conditions of scattering and photon flux levels were simulated, and reconstruction of the degraded images is performed using two linear reconstruction algorithms: a Wiener filter and a constrained least squares filter. Results of the reconstructions show that spatial resolution can be recovered in badly blurred images up to the limit imposed by the noise effective cutoff spatial frequency of the measurement.
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