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23 December 2002 Comparison of reconstruction algorithms for images from sparse-aperture systems
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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.
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James R. Fienup, Douglas K. Griffith, L. Harrington, A. M. Kowalczyk, Jason J. Miller, and James A. Mooney "Comparison of reconstruction algorithms for images from sparse-aperture systems", Proc. SPIE 4792, Image Reconstruction from Incomplete Data II, (23 December 2002); https://doi.org/10.1117/12.452396
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