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Proceedings Paper

Quantifying the super-resolution capabilities of the CLEAN image processing algorithm
Author(s): Bobby R. Hunt
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Paper Abstract

The problem of image restoration has an extensive literature (e.g.J1^^’^) and can be expressed as the solution of an integral equation of the first kind. Conventional linear restoration methods reconstruct spatial frequencies below the diffraction-limited cutoff of the optical aperture. Nonlinear methods, such as maximum entropy^, have the potential to reconstruct frequencies above the diffraction limit. Reconstruction of information above diffraction we refer to as super-resolution. Specific algorithms developed for super-resolution are the iterative algorithms of Gerchbergf5! and Papoullis^, the maximum likelihood method of Holmes^, and the Poisson maximum-a-posteriori algorithm of Hunrf8^. The experimental results published with these algorithms show the potential of super-resolution, but are not as satisfactory as an analytical treatment. In the following we present a model to quantify the capability of super-resolution, and discuss the model in the context of the well-known CLEAN algorithm.

Paper Details

Date Published: 12 January 1993
PDF: 7 pages
Proc. SPIE 1771, Applications of Digital Image Processing XV, (12 January 1993); doi: 10.1117/12.139074
Show Author Affiliations
Bobby R. Hunt, Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 1771:
Applications of Digital Image Processing XV
Andrew G. Tescher, Editor(s)

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