Statistics – Computation
Scientific paper
Jul 1996
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1996josaa..13.1537b&link_type=abstract
Journal of the Optical Society of America A, Vol. 13, No. 7, p. 1537 - 1545
Statistics
Computation
4
Image Restoration: Hubble Space Telescope
Scientific paper
The authors 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. They demonstrate that SV-PSF restoration exhibits superior performance compared with restoration with a spatially invariant point-spread function. The authors 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. They 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.
Boden Andrew F.
Hanisch Robert J.
Mo Jianhua
Redding Dave C.
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