Multiframe blind deconvolution of astronomical images

Maximum-likelihood estimation techniques are presented for the problem of forming object estimates from turbulence-degraded images when the point-spread functions are unknown. The inability of unconstrained maximum-likelihood methods to form meaningful estimates is acknowledged, and iterative algorithms are derived for estimating the object by using both a penalized maximum-likelihood method and a physically meaningful parameterization of the point-spread functions by phase errors distributed over an aperture.