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Proceedings Paper

Comparison of reconstruction algorithms for images from sparse-aperture systems
Author(s): James R. Fienup; Douglas K. Griffith; L. Harrington; A. M. Kowalczyk; Jason J. Miller; James A. Mooney
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Paper Abstract

Telescopes and imaging interferometers with sparsely filled apertures can be lighter weight and less expensive than conventional filled-aperture telescopes. However, their greatly reduced MTF’s cause significant blurring and loss of contrast in the collected imagery. Image reconstruction algorithms can correct the blurring completely when the signal-to-noise (SNR) is high, but only partially when the SNR is low. This paper compares both linear (Wiener) and nonlinear (iterative maximum likelihood) algorithms for image reconstruction under a variety of circumstances. These include high and low SNR, Gaussian noise and Poisson-noise dominated, and a variety of aperture configurations and degrees of sparsity. The quality metric employed to compare algorithms is image utility as quantified by the National Imagery Interpretability Rating Scale (NIIRS). On balance, a linear reconstruction algorithm with a power-law power-spectrum estimate performed best.

Paper Details

Date Published: 23 December 2002
PDF: 8 pages
Proc. SPIE 4792, Image Reconstruction from Incomplete Data II, (23 December 2002); doi: 10.1117/12.452396
Show Author Affiliations
James R. Fienup, Institute of Optics/Univ. of Rochester (United States)
Douglas K. Griffith, Veridian Systems (United States)
L. Harrington, Veridian Systems (United States)
A. M. Kowalczyk, Veridian Systems (United States)
Jason J. Miller, Veridian Systems (United States)
James A. Mooney, Eastman Kodak Co. (United States)

Published in SPIE Proceedings Vol. 4792:
Image Reconstruction from Incomplete Data II
Philip J. Bones; Michael A. Fiddy; Rick P. Millane, Editor(s)

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