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Orthogonal M-band compactly supported interpolating wavelet theory

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Abstract

Recently, 2-band interpolating wavelet transform has attracted much attention. It has the following several features: (i) The wavelet series transform coefficients of a signal in the multiresolution subspace are exactly consistent with its discrete wavelet transform coefficients; (ii) good approximation performance; (iii) efficiency in computation. However orthogonal 2-band compactly supported interpolating wavelet transform is only the first order. In order to overcome this shortcoming, the orthogonal M-band compactly supported interpolating wavelet basis is established. First, the unitary interpolating scaling filters of the lengthL =MK are characterized. Second, a scheme is given to design high-order unitary interpolating scaling filters. Third, a parameterization of the unitary interpolating scaling filters of the lengthL = 4M is made. Fourth, the orthogonal 2-order and 3-order three-band compactly supported interpolating scaling functions are constructed. Finally, the properties of the orthogonal M-band compactly supported interpolating wavelets and the approximation performance of the Mallat projection are discussed. For the smooth signal inL 2(ℝ), the asymptotic formula of the approximation enor of the Mallat projection is obtained, and for the band-limited signal, the quantitative estimate of its upper bounds is given. The results show that the Mallat projection has the same approximation order as the orthogonal projection, and particularly for the orthogonal even number-order M-band compactly supported interpolating scaling function, they have the same approximation performance. The quantitative result also shows that the selection of the initial scale depends on the distribution of the signal frequency and the regularity order of the scaling function. For the given scaling function and signal, using these results one can determine the initial scale and at the same time estimate the initial scaling coefficients without prefiltering according to the error requirement

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References

  1. Dubuc, S., Interpolating through an iterative scheme,Journal of Mathematical Analysis and Applications, 1986, 114: 185.

    Article  MATH  MathSciNet  Google Scholar 

  2. Xia, X. G., Zhang, Z., On sampling theorem, wavelets, and wavelet transforms,IEEE Trans. Signal Processing, 1993, 41(12): 3524.

    Article  MATH  Google Scholar 

  3. Saito, N., Beylkin, G., Multiscale representations using the auto-correlation functions of compactly supported wavelets,IEEE Trans. Signal Processing, 1993, 41(12): 3584.

    Article  MATH  Google Scholar 

  4. Swelden, W., The lifting scheme: a custom-design construction of biorthogonal wavelets,Appl. and Comput. Harmonic Anal. 1996, 3(2): 186.

    Article  Google Scholar 

  5. Zou, H., Tewfik, A. H., Discrete orthonormal M-band wavelet decomposions, inPro. Int Conf. Acoust., Speech, Sig. Proc., Vol. 4, San Francisco, CA, 1992, 4: 605–608.

  6. Steffen, P., Heller, P., Gopinath, R. A. et al. Theory of regular M-band wavelet bases,IEEE Trans. Signal Processing, 1993, 41(12): 3497.

    Article  MATH  Google Scholar 

  7. Alkin, Q., Caglar, H., Design of efficient M-band coders with linear-phase and perfect-reconstruction properties,IEEE Trans. Signal Processing, 1995, 43(7): 1579.

    Article  Google Scholar 

  8. Zhang, J. K., Bao, Z., Three-band compactly supported orthogonal interpolating scaling function,Electronics Letters, 1998, 34(5): 451.

    Article  Google Scholar 

  9. Zhang, J. K., Bao, Z., Property of vanishing moments of orthogonal M-band compactly supported interpolating scaling function,IEE. Electron. Lett., 1998, 34(20): 1917.

    Article  Google Scholar 

  10. Walter, G. G., A sampling theorem for wavelet subspaces,IEEE Trans. Informat. Theory, 1992, 38(2): 881.

    Article  Google Scholar 

  11. Shensa, M. J., The discrete wavelet transform: wedding the atrous and Mallat algorithm,IEEE Trans. Signal Processing, 1992, 40(10): 2464.

    Article  MATH  Google Scholar 

  12. Lawton, W. M., Necessary and sufficient conditions for constructing orthonormal wavelet bases,J. Math. Physics, 1991, 32(1): 57.

    Article  MATH  MathSciNet  Google Scholar 

  13. Mallat, S. G., Multiresolution approximation and wavelets,Trans. of Amer. Math. Soc., 1989, 315: 69.

    Article  MATH  MathSciNet  Google Scholar 

  14. Unser, M., Approximation power of biorthogonal wavelet expansions,IEEE Trans. Signal Processing, 1996, 44(3): 519.

    Article  Google Scholar 

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Zhang, J., Bao, Z. Orthogonal M-band compactly supported interpolating wavelet theory. Sci. China Ser. E-Technol. Sci. 42, 567–583 (1999). https://doi.org/10.1007/BF02916993

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