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Telecommunications and Radio Engineering
SJR: 0.202 SNIP: 0.2 CiteScore™: 0.23

ISSN Imprimer: 0040-2508
ISSN En ligne: 1943-6009

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Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v64.i11.20
pages 901-909

Regularization and Enhanced in Radar Images Via Fusing the Maximum Entropy and Variational Analysis Methods (MEVA)

L. J. Morales-Mendoza
CINVESTAV-IPN, Prolong. López Mateos Sur No. 590, Gdl., Jalisco, Mexico
R. F. Vazquez-Bautista
CINVESTAV-IPN, Prolong. López Mateos Sur No. 590, Gdl., Jalisco, Mexico
Jose A. Andrade-Lucio
Facultad de Ingenieria Mecánica, Eléctrica y Electrónica, Universidad de Guanajuato. A.P. 215-A, 36730. Salamanca, Gto., México
Oscar G. Ibarra-Manzano
Guanajuato University, FIMEE, 36730, Salamanca, Gto, Mexico

RÉSUMÉ

In this article, we present a new fusion strategy for aggregating both the regularization and the anisotropic diffusion paradigms in radar mages reconstruction. The fusion is mainly addressed to gain the highlight features that are involved, in this case, the robust error norm for Variational Analysis (VA) method and the regularized Maximum Entropy (ME) method-based degrees of freedom. The fused method is so-called the Maximum Entropy-Variational Analysis method (MEVA). The method is developed and computational implemented using the modified Hopfield neural network. Furthermore, we present several selected computer simulation examples where real images are addressed to illustrate the outstanding usefulness of this method.


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