The Discrete Wavelet Transform: Wedding the .

In a general sense this paper represents an effort to clarify the relationship of discrete and continuous wavelet transforms. More narrowly, it focuses on bringing together two separately motivated implementations of the wavelet trans- form, the algorithme U trous and Mallat's multiresolution de- composition. It is observed that these algorithms are both spe- cial cases of a single filter bank structure, the discrete wavelet transform, the behavior of which is governed by one's choice of filters. In fact, the h trow algorithm, originally devised as a computationally efficient implementation, is more properly viewed as a nonorthonormal multiresolution algorithm for which the discrete wavelet transform is exact. Moreover, it is shown that the commonly used Lagrange i~ trous filters are in one-to-one correspondence with the convolutional squares of the Daubechies filters for orthonormal wavelets of compact support. A systematic framework for the discrete wavelet transform is provided, and conditions are derived under which it com- putes the continuous wavelet transform exactly. Suitable filter constraints for finite energy and boundedness of the discrete transform are also derived. Finally, relevant signal processing parameters are examined, and it is remarked that orthonor- mality is balanced by restrictions on resolution.

[1]  I. Daubechies,et al.  Biorthogonal bases of compactly supported wavelets , 1992 .

[2]  A. Grossmann,et al.  DECOMPOSITION OF HARDY FUNCTIONS INTO SQUARE INTEGRABLE WAVELETS OF CONSTANT SHAPE , 1984 .

[3]  D. Esteban,et al.  Application of quadrature mirror filters to split band voice coding schemes , 1977 .

[4]  M. Shensa The Discrete Wavelet Transform , 1991 .

[5]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  G. Gambardella A contribution to the theory of short-time spectral analysis with nonuniform bandwidth filters , 1971 .

[7]  J. Morlet,et al.  Wave propagation and sampling theory—Part I: Complex signal and scattering in multilayered media , 1982 .

[8]  R. Altes Sonar for generalized target description and its similarity to animal echolocation systems. , 1976, The Journal of the Acoustical Society of America.

[9]  Martin Vetterli,et al.  A theory of multirate filter banks , 1987, IEEE Trans. Acoust. Speech Signal Process..

[10]  Daniel R. Fuhrmann,et al.  The Frazier-Jawerth transform , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[11]  Richard Kronland-Martinet,et al.  Reading and Understanding Continuous Wavelet Transforms , 1989 .

[12]  Martin Vetterli,et al.  Wavelets and filter banks: relationships and new results , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[13]  I. Daubechies Orthonormal bases of compactly supported wavelets II: variations on a theme , 1993 .

[14]  Mark J. T. Smith,et al.  Exact reconstruction techniques for tree-structured subband coders , 1986, IEEE Trans. Acoust. Speech Signal Process..

[15]  J. Kaiser,et al.  Wavelet construction using Lagrange halfband filters , 1991 .

[16]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[17]  Martin Vetterli,et al.  Wavelets and filter banks: theory and design , 1992, IEEE Trans. Signal Process..

[18]  Christopher Heil,et al.  Continuous and Discrete Wavelet Transforms , 1989, SIAM Rev..

[19]  Stéphane Mallat,et al.  Multifrequency channel decompositions of images and wavelet models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[20]  Richard Kronland-Martinet,et al.  Analysis of Sound Patterns through Wavelet transforms , 1987, Int. J. Pattern Recognit. Artif. Intell..

[21]  S. Mallat Multiresolution approximations and wavelet orthonormal bases of L^2(R) , 1989 .

[22]  O. Herrmann On the approximation problem in nonrecursive digital filter design , 1971 .