Noise removal from image data using recursive neurofuzzy filters

Neurofuzzy approaches are very promising for nonlinear filtering of noisy i mages. An original network topology is presented in this work to cope with different noise distributions and mixed noise as well. The multiple-output structure is based on recursive processing, It is able to adapt the filteri ng action to different kinds of corrupting noise. Fuzzy reasoning embedded into the network structure aims at reducing errors when fine details are pr ocessed. Genetic learning yields the appropriate set of network parameters from a collection of training data. Experimental results show that the prop osed neurofuzzy technique is very effective and performs significantly bett er than well-known conventional methods in the literature.