Download Nonlinear Digital Filters: Principles and Applications by I. Pitas, A. N. Venetsanopoulos (auth.) PDF

By I. Pitas, A. N. Venetsanopoulos (auth.)

The functionality of a clear out is to rework a sign into one other yet another swimsuit­ capable for a given goal. As such, filters locate functions in telecommunica­ tions, radar, sonar, distant sensing, geophysical sign processing, photo professional­ cessing, and machine imaginative and prescient. a number of authors have thought of deterministic and statistical methods for the learn of passive, lively, electronic, multidimen­ sional, and adaptive filters. many of the filters thought of have been linear even if the idea of nonlinear filters is constructing swiftly, because it is obvious via the various learn papers and some really good monographs now to be had. Our examine pursuits during this zone created chance for cooperation and co­ authored courses in past times few years in lots of nonlinear clear out households defined during this ebook. because of this cooperation and a trip from John Pitas on a learn go away on the collage of Toronto in September 1988, the assumption for this e-book used to be first conceived. the trouble in writing any such mono­ graph was once that the world appeared fragmented and no basic conception was once to be had to surround the numerous other forms of filters offered within the literature. although, the similarities of a few households of nonlinear filters and the necessity for this sort of monograph delivering a wide evaluation of the full region made the professional­ ject worthy. the result's the e-book now on your fingers, typeset on the division of electric Engineering of the college of Toronto in the course of the summer time of 1989.

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1) The noise mean and standard deviation are denoted by Jlv,av, respectively. 2) The value P=l/3 has also been suggested as more realistic [2, p. 21]. 3) The film-grain noise does not show statistical correlation for distances between samples greater than the grain size. Therefore, film-grain noise is a white-noise two-dimensional random process. 1-2) show that film-grain noise is signal-dependent noise. 4) where n(x,y) is a two-dimensional random Gaussian process with zero mean and unit variance.

Ronchetti, P. Rousseeuw, W. Stahel, Robust statistics, John Wiley, 1986. J. Justusson, "Median filtering: Statistical properties", in Topics in applied physics, vol. S. Huang editor, Springer Verlag, 1981. P. J. Huber, "Robust estimation of a location parameter", Ann. Math. 35, pp. 73-101, 1964. D. F. Andrews, P. J. Bickel, F. R. Hampel, P. S. Huber, W. H. Rogers, J. W. Tukey, Robust estimates of location: Survey and advances, Princeton [7] [8] University Press, 1972. [9] P. J. Bickel, "One step Huber estimates in the linear model", J.

22) The range is very sensitive in the presence of outliers, whereas the quasi-range and the thickened range are not. Another unbiased estimator that is not as influenced by outliers is the following [1, p. 191]: " cr= 2{7t II , 1 ( _ ) [l-- (n+1)]x(i) n n 1 •=! 2 l:. 23) Other interesting variants are also given in [1, p. 192]: G= f (n-2i+1)W(i) [~) n(n-1) i=l G= ( 2 11-1. 8 R-ESTIMA TORS The R-estimators have been proposed by Hodges and Lehmann (1963). The name R-estimator comes from the rank R; =r;( x ) of an observation x.

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