Massively parallel spatially variant maximum-likelihood restoration of Hubble Space Telescope imagery

Abstract

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