Genetic-based fuzzy hybrid multichannel filters for color image restoration

Abstract

On the design of multichannel filters, especially in color image restoration, it is not easy to simultaneously achieve three objectives: noise attenuation, chromaticity retention, and edges or details preservation. In this paper we propose a new class of multichannel filters, called genetic-based fuzzy hybrid multichannel (GFHM) filters, to reach these three objectives simultaneously. The design of GFHM filters is mainly based on human concept (heuristic rules) and genetic algorithms. Because the human concept can be readily and efficiently expressed by fuzzy implicative rules, GFHM filters can take the useful characteristics of filtering behavior of three filters: a vector median, a vector directional, and an identity filter. Since genetic algorithms possess the global-searching capability for an optimal solution, they are able to effectively optimize GFHM filters to improve the filtering performance. In color image restoration applications, extensive simulation results illustrate that GFHM filters not only achieve these three objectives but also possess the robust and the adaptive capability; moreover, these simulation results also demonstrate that the performance of GFHM filters outperforms that of other proposed filtering techniques.