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Proceedings Paper

Use Of Matched Filtering To Identify Speckle Locations
Author(s): E. Ribak; E. K. Hege; J. C. Christou
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Paper Abstract

In many cases the speckle maxima required for the our realization of the Shift-and-Add method are not well defined. This is due mainly to Poisson noise, inherent in the detection process, which obliterates the shape of faint speckles. The problem is aggravated for extended objects with local peaks, such as binary stars. As a remedy, we use a filter that smoothes out each speckle and at the same time defines its location. The best filter should be very close to the mean speckle itself: a matched filter. The initial guess for this filter is a bell function, slightly wider than the expected mean speckle. This initial guess is used to locate filtered speckle maxima which are then used to produce a better mean speckle estimate by shift-and add. The procedure is iterated until the mean speckle converges. We find that the iterative speckle estimate is not the optimum matched filter. The most suitable filter must suppress the variable background created by coalescing speckles in a large speckle cloud as well as smooth the single-photon event noise. Thus we combine the mean speckle with a band-pass filter into a matched filter. Local speckle maxima are thus enhanced, whereas single photons are discriminated against by using a comparison low-pass filtered frame, since they do not contain much power. The combined process, speckle identification and weighted-shift-and-add, can be carried out in the image plane or in the Fourier plane. We have experimented in both domains.

Paper Details

Date Published: 25 November 1985
PDF: 6 pages
Proc. SPIE 0556, Intl Conf on Speckle, (25 November 1985); doi: 10.1117/12.949540
Show Author Affiliations
E. Ribak, University of Arizona (United States)
E. K. Hege, Kitt Peak National Observatory (United States)
J. C. Christou, New Mexico State University (United States)


Published in SPIE Proceedings Vol. 0556:
Intl Conf on Speckle
Henri H. Arsenault, Editor(s)

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