A family of nonlinear filters with data dependent coefficients

A new family of data dependent nonlinear filters (DDNL) is presented. Coefficients are computed locally and the absolute distance between signal samples is used to determine a rank order dependent weighting function. The new filters have very good performance regarding noise suppression and impulse rejection and at the same time provide better edge preservation than the averager and other linear smoothers.<
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