Noise Masking for Pattern Recall Using a Single Lattice Matrix Auto-Associative Memory

Lattice matrix associative memories have been developed as an alternative way to work with a set of associated pattern pairs for which the storage and retrieval stages are based in the theory of minimax algebra. Several methods have been proposed to cope with the problem of binary or real valued pattern recall from corrupted inputs and recent results on fixed point sets of matrix lattice transforms have provided for an algebraic characterization as well as a geometrical description of the canonical lattice min/max auto-associative memories. Compared to other correlation type associative memory models, the lattice associative memory schemes have shown better performance for both storage and recall capability; however, the computational techniques devised to achieve that purpose are still cumbersome when inputs have undetermined noise bounds. The procedure suggested in this paper uses noise masking to boost the recall performance of either the min or max morphological auto-associative memories. Examples using image patterns show the enhanced recovery of almost correct associations from noisy inputs by a single lattice matrix memory.