IEEE Transactions on Signal Processing · 1998 · 95 citations · 24 references
EngineeringElectrical EngineeringMultiscale ModelingPhoneticsPsychoacousticsDigital Signal ProcessingSignal ProcessingTransfer FunctionsApproximation PropertiesDiscrete Wavelet TransformFilter Bank DesignWavelet AnalysisWavelet TransformWavelet-based Analysis
This paper highlights the differences between traditional wavelet and multiwavelet bases with equal approximation order. Because multiwavelet bases normally lack important properties that traditional wavelet bases (of equal approximation order) possess, the associated discrete multiwavelet transform is less useful for signal processing unless it is preceded by a preprocessing step (prefiltering). This paper examines the properties and design of orthogonal multiwavelet bases, with approximation order >1 that possess those properties that are normally absent. For these "balanced" bases (so named by Lebrun and Vetterli (see Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), Munich, Germany, vol.3, p.2473-76, 1997 and ibid., vol.46, p.1119-24, 1998)), prefiltering can be avoided. By reorganizing the multiwavelet filter bank, the development in this paper draws from results regarding the approximation order of M-band wavelet bases. The main result thereby obtained is a characterization of balanced multiwavelet bases in terms of the divisibility of certain transfer functions by powers of (z/sup -2r/-1)/(z/sup -1/-1). For traditional wavelets (r=1), this specializes to the usual factor (z+1)/sup K/.
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Noise reduction using an undecimated discrete wavelet transform
Markus Lang, Jan E. Odegard, C.S. Burrus et al. · IEEE Signal Processing Letters · 1996 · 485 citations