Iterative image restoration using approximate inverse preconditioning.

Details Iterative image restoration using approximate inverse preconditioning. Iterative image restoration using approximate inverse preconditioning.

Jul 1996

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1996itip....5.1151n&link_type=abstract

IEEE Trans. Image Process., Vol. 5, No. 7, p. 1151 - 1162

Astronomy and Astrophysics

Astronomy

9

Image Restoration: Astronomy

Scientific paper

Removing a linear shift-invariant blur from a signal or image can be accomplished by inverse or Wiener filtering, or by an iterative least-squares deblurring procedure. Because of the ill-posed characteristics of the deconvolution problem, in the presence of noise, filtering methods often yield poor results. On the other hand, iterative methods often suffer from slow convergence at high spatial frequencies. This paper concerns solving deconvolution problems for atmospherically blurred images by the preconditioned conjugate gradient algorithm, where a new approximate inverse preconditioner is used to increase the rate of convergence. Theoretical results are established to show that fast convergence can be expected, and test results are reported for a ground-based astronomical imaging problem.

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