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Volume 23 Issue 11
Nov.  2001
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Guo Bingqing, Lu Qiang, Li Xiong . A KIND OF DETAIL-PRESERVING ADAPTIVE FILTER BASED ON LOCAL SIGNAL TIME-VARYING CHARACTERISTICS[J]. Journal of Electronics & Information Technology, 2001, 23(11): 1050-1055.
Citation: Guo Bingqing, Lu Qiang, Li Xiong . A KIND OF DETAIL-PRESERVING ADAPTIVE FILTER BASED ON LOCAL SIGNAL TIME-VARYING CHARACTERISTICS[J]. Journal of Electronics & Information Technology, 2001, 23(11): 1050-1055.

A KIND OF DETAIL-PRESERVING ADAPTIVE FILTER BASED ON LOCAL SIGNAL TIME-VARYING CHARACTERISTICS

  • Received Date: 2000-04-06
  • Rev Recd Date: 2000-10-16
  • Publish Date: 2001-11-19
  • Authors, in this paper, propose and analyze a new kind of detail-preserving adaptive filter algorithm according to relations between local temporal signal and its order statistics. Theoretical analysis and simulation results show that the adaptive filters determined by similarity measurement between temporal signal and monotonic order statistics which belongs to the root signal set of standard median filter have good performances in the aspects of removal of complicated distributed noises as well as preservation of signal edges and details.
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  • I. Pitas, A. N. Venetsanopoulos, Nonlinear Digital Filters, Norwell, MA: Kluwer, 1990, ch.3. [2]C.L. Nikas, A. P. Petropulu, Higher-Order Spectra Analysis, Englewood Cliffs, NJ, Prentice-Hall,1993, ch.4.[2]J. Mathews, Adaptive polynomial filters, IEEE Signal Processing Mag. , 1991, 8(4), 10-26.[3]T.H. Koh, E. J. Powers, Second-order Volterra filtering and its application to nonlinear systemidentification, IEEE Trans. on ASSP, 1985, 33(6), 1445-1455.[4]B.J. Justusson, Median Filtering: Statistic Properties in Two-Dimensional Digital Signal Pro-cessing, T. S. Huang Ed., New York, Springer-Verlag, 1981, vol.2, ch.4.[5]A.C. Bovik, T. S. Huang, D. C. Munson, A generalization of median filtering using linear combinations of order statistics, IEEE Trans. on ASSP, 1983, 31(6), 1342 1350.[6]G.R. Arce, N. C. Gallagher, T. Nodes, Median Filters: Theory and Applications, Advances in Computer Vision and Image Processing, T. S. Huang Ed. Greenwich, CT: JAI Press, 1986, ch.5. [8]G. R. Arce, R. E. Foster, Detail-preserving ranked-order based filters for inage processing, IEEE Trans. on ASSP, 1989, 37(1), 83-98.[7]R. Bernstein, Adaptive nonlinear filters for simultaneous removal of different kinds of noise inimages, IEEE Trans. on CAS, 1987, 34(11), 1275 1291.[8]X.Z. Sun, A. N. Venetsanopoulos, Adaptive schemes for noise filtering and edge detection by use of local statistics, IEEE Trans. on CAS, 1988, 35(1), 57 69.[9]P. Salembier, Adaptive rank order based filters, Signal Processing, 1992, 27(1), 1 25.[10]K.E. Barner, G. R. Arce, Permutation Filters: A class of nonlinear filters based on set permuta-tion, IEEE Trans. on SP, 1994, 42(4), 782 798.[11]Y.T. Kim , G. R. Arce, Permutation filter lattices: A general order-statistic filtering framework, IEEE Trans. on SP, 1994, 42(9), 2227-2241.[12]G.R. Arce, T. A. Hall, K. E. Barner, Permutation weighted order statistic filter lattices, IEEE Trans. on IP, 1995, 4(8), 1070-1083.[13]G.R. Arce, N. C. Gallagher, State description for root signal set of median filter, IEEE Trans.on ASSP, 1982, 30(4), 894-902.[14]J. P. Fitch , E. J. Coyle et al., Root properties and convergence rates of median filter, IEEETrans. on ASSP, 1985, 33(1), 230 240.
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