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. !15
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