Image Filtering Using Evolutionary Neural Fuzzy Systems

Evolutionary neural fuzzy filters are a new class of nonlinear filters for image processing. The original network structure of these filters adopts fuzzy reasoning in order to cancel noise without destroying fine details and textures. The learning method based on the Genetic Algorithms yields very satisfactory results within a few generations. Experimental results have shown that evolutionary neural fuzzy filters are very effective in removing impulse noise from highly corrupted images and significantly outperform conventional techniques. This chapter aims at providing a detailed description of the network architecture of these filters focusing on fuzzy set-based operations, encoding schemes and training procedures.