Statistically-efficient filtering in impulsive environments: weighted myriad filters
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Signal Processing
Fast and Accurate Computation of the Myriad Filter via Branch-and-Bound Search
IEEE Transactions on Signal Processing - Part II
Fast algorithms for weighted myriad computation by fixed-pointsearch
IEEE Transactions on Signal Processing
Meridian Filtering for Robust Signal Processing
IEEE Transactions on Signal Processing
Image denoising: a nonlinear robust statistical approach
IEEE Transactions on Signal Processing
Optimality of the myriad filter in practical impulsive-noiseenvironments
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Robust frequency-selective filtering using weighted myriad filtersadmitting real-valued weights
IEEE Transactions on Signal Processing
Alpha-stable modeling of noise and robust time-delay estimation inthe presence of impulsive noise
IEEE Transactions on Multimedia
M-estimator and D-optimality model construction using orthogonal forward regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Impulsive noise cancelation with simplified Cauchy-based p-norm filter
Signal Processing
| Hi-index | 0.08 |
The myriad filter is a robust filter which is very useful in suppressing impulsive noise. It belongs to the family of robust M-filters and is controlled with one parameter only. The myriad filter is defined as a running window filter whose output is the sample myriad of the elements in the window. The most popular approach to determination of myriad filter output is based on the fixed point method. However, in this case time of computation is relatively long, which limits on-line applications. In this paper, a faster method of the myriad filter computation is presented. It performs the second order polynomial fitting and the next x-coordinate of the parabola top is searched. The proposed method can operate on different types of impulsive noise, requires less computational time and is equally robust as the fixed point method or the branch-and-bound search method. The presented method is applied to process a chirp signal in impulsive environment. The obtained results are compared to the fixed-point algorithm and the branch-and-bound searching method.