Neural image processing filters provide an efficient means for synthesis of high-order nonlinear systems. Parallel hardware implementations are feasible. A modular design methodology for analysis and synthesis of nonlinear neural network filters is described. This method is based on a building block approach: subnetworks are designed separately and assembled via linear connecting layers. Efficient neural filters are designed by removing the connecting layers. Classical image processing filters such as Volterra, order statistic, and morphological filters are synthesized using large multilayer networks.
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