Evolution of efficient real-time non-linear image filters for FPGAs

Image processing represents a research field in which high-quality solutions have been obtained using various soft computing techniques. Evolutionary algorithms constitute a class of stochastic search methods that are applicable in both optimization and design tasks. In the area of circuit design Cartesian Genetic Programming has often been utilized in combination with an algorithm of Evolutionary Strategy. Digital image filters represent a specific class of circuits whose design can be performed by means of this approach. Switching filters are advanced non-linear filtering techniques in which the main idea is to detect and filter the noise pixels while keeping the uncorrupted pixels unchanged in order to increase the quality of the resulting image. The aim of this article is to present a robust design technique based on Cartesian Genetic Programming for the automatic synthesis of switching image filters intended for real-time processing applications. The robustness of the proposed evolutionary approach is evaluated using four design problems including the removal of salt and pepper noise, random shot noise, impulse burst noise and impulse burst noise combined with random shot noise. An extensive evaluation is performed in order to compare the properties of the evolved switching filters with the best conventional solutions. The evaluation has shown that the evolved switching filters exhibit a very good trade off between the quality of filtering and the implementation cost in field programmable gate arrays.