A fuzzy model-based neural network for adaptive regularization in image restoration

We address the problem of adaptive regularization in image restoration by adopting a neural network learning approach. The local regularization parameter values are regarded as network weights which are then modified through the supply of appropriate training examples. We also consider the separate regularization of edges and textures due to their different noise masking capabilities, which in turn requires discrimination between these two feature types. A new edge-texture characterization (ETC) measure is derived and incorporated into a fuzzified form of the previous NN for the above purpose