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

摘要

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.