Design and hardware synthesis of adaptive weighted fuzzy mean image filter

Abstract

This invention proposes a novel nonlinear filter called adaptive weighted fuzzy mean (AWFM) filter which is capable of removing high density Gaussian impulse noises in polluted images. Via a single epoch, weights of the filter are departed according to a set of fuzzy rules, which are constructed by a proposed procedure. All the fuzzy rules in the knowledge base infer concurrently for input nature estimation and filter adaptation. Statistic analyses ensure that this AWFM filtering possesses robust and consistent performance even when images are thoroughly polluted. The AWFM filter is synthesized with generic LR fuzzy cells which adopts CMOS analog current mode technology to subject high speed, low power, and small circuit area objectives. Simulations show that parallel inferences realized by the generic LR fuzzy cells, each of which performs 20 M FLIPS (fuzzy logic inferences per second) promise that the synthesized AWFM filter is able to process up to 50 256×256 images per second.