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Proceedings Paper

Constraining information for improvements to MAP image super-resolution
Author(s): Patrick Wingkee Yuen; Bobby R. Hunt
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Paper Abstract

In this paper, the super-resolution method that we use for image restoration is the Poisson Maximum A-Posteriori (MAP) super-resolution algorithm of Hunt, computed with an iterative form. This algorithm is similar to the Maximum Likelihood of Holmes, which is derived from an Expectation/Maximization (EM) computation. Image restoration of point source data is our focus. This is because most astronomical data can be regarded as multiple point source data with a very dark background. The statistical limits imposed by photon noise on the resolution obtained by our algorithm are investigated. We improve the performance of the super-resolution algorithm by including the additional information of the spatial constraints. This is achieved by applying the well-known CLEAN algorithm, which is widely used in astronomy, to create regions of support for the potential limited optical system is used for the simulated data. The real data is two dimensional optical image data from the Hubble Space Telescope.

Paper Details

Date Published: 30 September 1994
PDF: 12 pages
Proc. SPIE 2302, Image Reconstruction and Restoration, (30 September 1994); doi: 10.1117/12.188036
Show Author Affiliations
Patrick Wingkee Yuen, Univ. of Arizona (United States)
Bobby R. Hunt, Univ. of Arizona (United States)

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

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