Selection weighted vector directional filters
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
A new adaptive center weighted median filter for suppressing impulsive noise in images
Information Sciences: an International Journal
Statistically-efficient filtering in impulsive environments: weighted myriad filters
EURASIP Journal on Applied Signal Processing
New polynomial approach to myriad filter computation
Signal Processing
An overview of the adaptive robust DFT
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
One dimensional nonlinear adaptive filters for impulse noise suppression
AEE'06 Proceedings of the 5th WSEAS international conference on Applications of electrical engineering
Impulsive noise cancelation with simplified Cauchy-based p-norm filter
Signal Processing
Efficient denoising of piecewise-smooth signals with forward-backward FIR smoothers
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
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Stochastic gradient-based adaptive algorithms are developed for the optimization of weighted myriad filters (WMyFs). WMyFs form a class of nonlinear filters, motivated by the properties of α-stable distributions, that have been proposed for robust non-Gaussian signal processing in impulsive noise environments. The weighted myriad for an N-long data window is described by a set of nonnegative weights {wi }i=lN and the so-called linearity parameter K>0. In the limit, as K→∞, the filter reduces to the familiar weighted mean filter (which is a constrained linear FIR filter). Necessary conditions are obtained for optimality of the filter weights under the mean absolute error criterion. An implicit formulation of the filter output is used to find an expression for the gradient of the cost function. Using instantaneous gradient estimates, an adaptive steepest-descent algorithm is then derived to optimize the weights. This algorithm involves a very simple update term that is computationally comparable to the update in the classical LMS algorithm. The robust performance of this adaptive algorithm is demonstrated through a computer simulation example involving lowpass filtering of a one-dimensional chirp-type signal in impulsive noise