Fast Algorithm for Order Statistics Filters

Yan Ting Chen; Wan Zhou Ye

doi:10.4028/www.scientific.net/AMM.651-653.2154

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 651-653Fast Algorithm for Order Statistics Filters

Fast Algorithm for Order Statistics Filters

Abstract:

Nonlinear processing techniques are very important tools in signal and image processing. One of the most significant classes is based on order statistics, which depends on data sorting algorithm. The sorting algorithm is very time consuming and intractable for vector data. Therefore, it is extremely urgent to put forward a fast algorithm to compute the r-th order statistics without sorting operation. The ITM and ITTM filter proposed an iteration algorithm to approach the median, from the mean. It is reasonab to generalize to the other order statistics by building a new data set Xp which is composed by the elements of the original set X. It makes the r-th order statistics of X to be the median of Xp. Then, estimating the median of the new set Xp by the ITTM algorithm, the output is estimation value of x_(r).

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Periodical:

Applied Mechanics and Materials (Volumes 651-653)

Pages:

2154-2158

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.651-653.2154

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Online since:

September 2014

Authors:

Yan Ting Chen, Wan Zhou Ye

Keywords:

Iteration Threshold, ITM Filter, ITTM Filter, Order Statistics Filter, α-Trimmed Mean Filter

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References

[1] J.W. Tukey: Exploratory Data Analysis. Reading, MA: Addison-Wesley, (1977).

Google Scholar

[2] J. B. Bednar and T. L. Watt: Alpha-trimmed means and their relationship to median filters, IEEE Trans. Acoust. Speech and Signal Process., vol. 32(1), 145-153 (1984).

DOI: 10.1109/tassp.1984.1164279

Google Scholar

[3] S. R. Peterson, Y. H. Lee, and S. A. Kassam: Some statistical properties of alpha-trimmed mean and standard type M filters, IEEE Trans. Acoust. Speech and Signal Process., vol. 36(5), 707-713 (1988).

DOI: 10.1109/29.1580

Google Scholar

[4] X.D. Jiang: Iterative truncated arithmetic mean filter and its properties, IEEE Trans. Image Process., vol. 21(4), 1537-1547 (2012).

DOI: 10.1109/tip.2011.2172805

Google Scholar

[5] Z. W. Miao, X .D. Jiang: Further properties and a fast realization of the iterative truncated arithmetic mean filter, IEEE Trans. II: Express Briefs 59(11), 810-814(2012).

DOI: 10.1109/tcsii.2012.2218473

Google Scholar

[6] Z. W. Miao, X. D. Jiang: Additive and exclusive noise suppression by iterative trimmed and truncated mean arithmetic, ELSEVIER, Signal Process., vol. 99, 147-158 (2014).

DOI: 10.1016/j.sigpro.2013.12.002

Google Scholar