Abstract
We propose FIR-median hybrid filters with polynomial fitting (HFPF) for nonlinear filtering applications. These techniques combine linear prediction and linear smoothing operations and median filtering to preserve the jump point and edge information in the signal, and at the same time to attenuate noise. The linear prediction and smoothing operations are based on polynomial fitting. Through numerical experiments, the proposed HFPF methods are shown to outperform the previously proposed FIR-median hybrid filters and the wavelet shrinkage algorithm in stationary and nonstationary Gaussian/impulsive noise environments.
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