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Optical Engineering

Image-spectrum signal-to-noise-ratio improvements by statistical frame selection for adaptive-optics imaging through atmospheric turbulence
Author(s): Michael C. Roggemann; Craig A. Stoudt; Byron M. Welsh
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Paper Abstract

Adaptive-optics systems have been used to overcome some of the effects of atmospheric turbulence on large-aperture astronomical telescopes. However, the correction provided by adaptive optics cannot restore diffraction-limited performance, due to discretized spatial sampling of the wavefront, limited degrees of freedom in the adaptive-optics system, and wavefront sensor measurement noise. Field experience with adaptive-optics imaging systems making short-exposure image measurements has shown that some of the images are better than others in the sense that the better images have higher resolution. This is a natural consequence of the statistical nature of the compensated optical transfer function in an adaptive-optics telescope. Hybrid imaging techniques have been proposed that combine adaptive optics and postdetection image processing to improve the high-spatial-frequency information of images. Performance analyses of hybrid methods have been based on prior knowledge of the ensemble statistics of the underlying random process. Improved image-spectrum SNRs have been predicted, and in some cases experimentally demonstrated. In this paper we address the issue of selecting and processing the best images from a finite data set of compensated short-exposure images. Image sharpness measures are used to select the data subset to be processed. Comparison of the image-spectrum SNRs for the cases of processing the entire data set and processing only the selected subset of the data shows a broad range of practical cases where processing the selected subset results in superior SNR.

Paper Details

Date Published: 1 October 1994
PDF: 11 pages
Opt. Eng. 33(10) doi: 10.1117/12.181250
Published in: Optical Engineering Volume 33, Issue 10
Show Author Affiliations
Michael C. Roggemann, Air Force Institute of Technology (United States)
Craig A. Stoudt, Air Force Institute of Technology (United States)
Byron M. Welsh, Air Force Institute of Technology (United States)

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