Next: Introduction
Iterative/Recursive Deconvolution with Application to HST Data
James M. Coggins
Department of Computer Science, CB 3175 Sitterson Hall, University
of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3175
Laura Kellar Fullton and Bruce W. Carney
Department of Physics and Astronomy, CB 3255 Phillips Hall, University
of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255
Abstract:
A new deblurring algorithm has been developed involving both iteration
and recursion and that is linear, flux-conserving, noise resistant, and
faster to converge than extant iterative deblurring methods. Mathematical
analysis shows that the recursive component of the algorithm provides the
accelerated convergence. A demonstration is provided using a simulated star
field image blurred using an approximation to the point spread function of
the Hubble Space Telescope.
Keywords:
iterative deconvolution, recursion, linear, flux-conserving,
noise-resistant