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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 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 Gerchberg and Papoullis, the maximum likelihood method of Holmes, and the Poisson maximum-a-posteriori algorithm of Hunt. 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 paper 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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