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Proceedings Paper

Reconstruction of complex signals using minimum Renyi information
Author(s): B. Roy Frieden; Anisa T. Bajkova
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Paper Abstract

An information divergence, such as Shannon mutual information, measures the `distance' between two probability density functions (or images). A wide class of such measures, called (alpha) -divergences, with desirable properties such as convexity over all space, has been defined by Amari. Renyi's information D(alpha ) is an (alpha) -divergence. Because of its convexity property, minimization of D(alpha ) is easily attained. Minimization accomplishes minimum distance (maximum resemblance) between an unknown image and a known, reference image. Such a biasing effect permits complex images, such as occur in ISAR imaging, to be well reconstructed. There, the bias image may be constructed as a smooth version of the linear. Fourier reconstruction of the data. Examples on simulated complex image data, with and without noise, indicate that the Renyi reconstruction approach permits super-resolution in low-noise cases, and higher fidelity over ordinary, linear reconstructions in higher-noise cases.

Paper Details

Date Published: 21 September 1994
PDF: 12 pages
Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); doi: 10.1117/12.186525
Show Author Affiliations
B. Roy Frieden, Optical Sciences Ctr./Univ. of Arizona (United States)
Anisa T. Bajkova, Institute of Applied Astronomy (Russia)

Published in SPIE Proceedings Vol. 2298:
Applications of Digital Image Processing XVII
Andrew G. Tescher, Editor(s)

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