Binary associative memories applied to gray level pattern recalling

In this paper we show how a binary memory can be used to recall gray-level patterns. Given a set of gray-level patterns to be first memorized: 1) Decompose each pattern into a set of binary patterns, and 2) Build a binary associative memory (one matrix for each binary layer) with each training pattern set (by layers). A given pattern or a distorted version of it is recalled in three steps: 1) Decomposition of the pattern by layers into its binary patterns, 2) Recovering of each one of its binary components, layer by layer also, and 3) Reconstruction of the pattern from the binary patterns already recalled in step 2. Conditions for perfect recall of a pattern either from the fundamental set or from a distorted version of one them are also given. Experiments are also provided.