Fast continuous wavelet transform: A least-squares formulation

M. J. Vrhel, C. Lee, M. Unser

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

We introduce a general framework for the efficient computation of the real continuous wavelet transform (CWT) using a filter bank. The method allows arbitrary sampling along the scale axis, and achieves O(N) complexity per scale where N is the length of the signal. Previous algorithms that calculated non-dyadic samples along the scale axis had O(N log (N)) computations per scale. Our approach approximates the analyzing wavelet by its orthogonal projection (least-squares solution) onto a space defined by a compactly supported scaling function. We discuss the theory which uses a duality principle and recursive digital filtering for rapid calculation of the CWT. We derive error bounds on the wavelet approximation and show how to obtain any desired level of accuracy through the use of longer filters. Finally, we present examples of implementation for real symmetric and anti-symmetric wavelets.

Original languageEnglish
Pages (from-to)103-119
Number of pages17
JournalSignal Processing
Volume57
Issue number2
Publication statusPublished - 1997 Mar 1

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Wavelet transforms
Filter banks
Sampling

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Vrhel, M. J. ; Lee, C. ; Unser, M. / Fast continuous wavelet transform : A least-squares formulation. In: Signal Processing. 1997 ; Vol. 57, No. 2. pp. 103-119.
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Vrhel, MJ, Lee, C & Unser, M 1997, 'Fast continuous wavelet transform: A least-squares formulation', Signal Processing, vol. 57, no. 2, pp. 103-119.

Fast continuous wavelet transform : A least-squares formulation. / Vrhel, M. J.; Lee, C.; Unser, M.

In: Signal Processing, Vol. 57, No. 2, 01.03.1997, p. 103-119.

Research output: Contribution to journalArticle

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