Quantifying the super-resolution capabilities of the CLEAN image processing algorithm

Bobby R. Hunt

doi:10.1117/12.139074

12 January 1993 Quantifying the super-resolution capabilities of the CLEAN image processing algorithm

Bobby R. Hunt

Author Affiliations +

Proceedings Volume 1771, Applications of Digital Image Processing XV; (1993) https://doi.org/10.1117/12.139074
Event: San Diego '92, 1992, San Diego, CA, United States

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.

Citation Download Citation

Bobby R. Hunt "Quantifying the super-resolution capabilities of the CLEAN image processing algorithm", Proc. SPIE 1771, Applications of Digital Image Processing XV, (12 January 1993); https://doi.org/10.1117/12.139074

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