Reconstruction of complex signals using minimum Renyi information

B. Roy Frieden; Anisa T. Bajkova

doi:10.1117/12.186525

21 September 1994 Reconstruction of complex signals using minimum Renyi information

B. Roy Frieden, Anisa T. Bajkova

Proceedings Volume 2298, Applications of Digital Image Processing XVII; (1994) https://doi.org/10.1117/12.186525
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

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.

Citation Download Citation

B. Roy Frieden and Anisa T. Bajkova "Reconstruction of complex signals using minimum Renyi information", Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); https://doi.org/10.1117/12.186525

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