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Proceedings Paper

Signal-to-noise comparison of deconvoution from wavefront sensing and speckle imaging
Author(s): Byron M. Welsh; Michael C. Roggemann
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Paper Abstract

It is well known that atmospheric turbulence severely degrades the performance of ground based imaging systems. Techniques to overcome the effects of the atmosphere have been developing at a rapid pace over the last 10 years. These techniques can be grouped into two broad categories: pre-detection and post detection techniques. A recent newcomer to the post detection scene is 'deconvolution from wave front sensing' (DWFS). DWFS is a post-detection image reconstruction technique that makes use of one feature of pre- detection techniques. A WFS is used to record the wave front phase distortion in the pupil of the telescope for each short exposure image. The additional information provided by the WFS is used to estimate of the system's point spread function (PSF). The PSF is then used in conjunction with the ensemble of short exposure images to obtain and estimate of the object intensity distribution via deconvolution. With the addition of DWFS into the suite of possible post detection image reconstruction techniques it is natural to ask 'How does DWFS compare to both traditional linear and speckle image reconstruction techniques?' In the results presented here we make a direct comparison based on a frequency domain signal-to-noise ration performance metric. This metric is applied to each technique's image reconstruction estimator. We find that such as Wiener filtering. On the other hand, DWFS does not always out perform speckle imaging techniques and in cases that it does the improvement is small.

Paper Details

Date Published: 30 September 1994
PDF: 12 pages
Proc. SPIE 2302, Image Reconstruction and Restoration, (30 September 1994); doi: 10.1117/12.188046
Show Author Affiliations
Byron M. Welsh, Air Force Institute of Technology (United States)
Michael C. Roggemann, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2302:
Image Reconstruction and Restoration
Timothy J. Schulz; Donald L. Snyder, Editor(s)

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