Partitioned architectures for large scale data recovery

Abstract

Thresholded binary networks of the Hopfield-type offer feasible configurations which are capable of recovering the regularized least-squares solution in certain inverse problem formulations. The proposed architectures and algorithms also permit hybrid electro-optical implementations. These architectures are determined from partitions of the original network and are based on forms of data representation. Sequential and parallel updates on these partitions are adopted to optimize the objective criterion. The algorithms consist of minimizing a suboptimal objective criterion in the currently active partition. Once the local minima is attained, an inactive partition is chosen to continue the minimization. An application to digital image restoration is considered.