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Proceedings Paper

Fast restoration of atmospherically blurred images
Author(s): James G. Nagy; Robert J. Plemmons; Todd C. Torgersen
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Paper Abstract

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. 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. Theoretical results are established to show that fast convergence for our iterative algorithm can be expected, and test results are reported for a ground-based astronomical imaging problem.

Paper Details

Date Published: 28 October 1994
PDF: 12 pages
Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); doi: 10.1117/12.190866
Show Author Affiliations
James G. Nagy, Southern Methodist Univ. (United States)
Robert J. Plemmons, Wake Forest Univ. (United States)
Todd C. Torgersen, Wake Forest Univ. (United States)

Published in SPIE Proceedings Vol. 2296:
Advanced Signal Processing: Algorithms, Architectures, and Implementations V
Franklin T. Luk, Editor(s)

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