RT Journal Article ID 2bf69aa635ff61d4 A1 Morales-Mendoza, L. J. A1 Vazquez-Bautista, R. F. A1 Andrade-Lucio, Jose A. A1 Ibarra-Manzano, Oscar G. T1 Regularization and Enhanced in Radar Images Via Fusing the Maximum Entropy and Variational Analysis Methods (MEVA) JF Telecommunications and Radio Engineering JO TRE YR 2005 FD 2006-12-12 VO 64 IS 7-12 SP 901 OP 909 AB 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. PB Begell House LK https://www.dl.begellhouse.com/journals/0632a9d54950b268,66fcef0f6e0d427b,2bf69aa635ff61d4.html