In recent years significant progress have been made in the development of nonlinear data processing techniques. Such techniques are widely used in digital data filtering and image enhancement. Many of the most effective nonlinear filters based on order statistics. The widely used median filter is the best known order statistic filter. Generalized form of these filters could be presented based on Lloyd's statistics. Filters based on order statistics have excellent robustness properties in the presence of impulsive noise. In this paper, we present special approach for synthesis of order statistics filters using artificial neural networks. Optimal Lloyd's statistics are used for selecting of initial weights for the neural network. Adaptive properties of neural networks provide opportunities to optimize order statistics filters for data with asymmetric distribution function. Different examples demonstrate the properties and performance of presented approach.

Design of order statistics filters using feedforward neural networks

A genetic back-propagation (BP) algorithm is used to solve the optimal design problem of multistage weighted order statistic filter under the mean absolute error criterion. It is shown experimentally that this algorithm can find the optimal WOS filter in image restoration application.

Yiming Pi

Papers

Design of multistage weighted order statistic filters by a neural network