Massively Parallel Spatially-Variant Maximum Likelihood Restoration of Hubble Space Telescope Imagery

We present results of concurrent maximum likelihood restoration implementations with spatially-variant point spread function (SV-PSF) on both synthetic and real datasets from the Hubble Space Telescope (HST). We demonstrate that SV-PSF restoration exhibits superior performance compared to restoration with a spatially invariant PSF. We realize concurrency on a network of Unix workstations, and a SV-PSF model from sparse PSF reference information by means of bilinear interpolation. We then use the interpolative PSF model to implement several diierent SV-PSF restoration methods. These restoration methods are tested on a standard synthetic Hubble Space Telescope test case, and the results are compared on a computational eeort/restoration performance basis. These methods are further applied to actual HST data, including an application that corrects for motion blur, and the results are presented.