Unsupervised blind deconvolution

To reduce the influence of atmospheric turbulence on images of space-based objects we are developing a maximum a posteriori deconvolution approach. In contrast to techniques found in the literature, we are focusing on the statistics of the point-spread function (PSF) instead of the object. We incorporated statistical information about the PSF into multi-frame blind deconvolution. Theoretical constraints on the average PSF shape come from the work of D. L. Fried while for the univariate speckle statistics we rely on the gamma distribution adopted from radar/laser speckle studies of J. W. Goodman. Our aim is to develop deconvolution strategy which is reference-less, i.e., no calibration PSF is required, extendable to longer exposures, and applicable to imaging with adaptive optics. The theory and resulting deconvolution framework were validated using simulations and real data from the 3.5m telescope at the Starfire Optical Range (SOR) in New Mexico.