Gaussianization
Scott Shaobing Chen, Ramesh A. Gopinath
NeurIPS 2000
This paper introduces an approximately shift invariant redundant dyadic wavelet transform - the phaselet transform - that includes the popular dual-tree complex wavelet transform of Kingsbury as a special case. The main idea is to use a finite set of wavelets that are related to each other in a special way - and hence called phaselets - to achieve approximate shift-redundancy; the bigger the set, the better the approximation. A sufficient condition on the associated scaling filters to achieve this is that they are fractional shifts of each other. Algorithms for the design of phaselets with a fixed number vanishing moments is presented - building on the work of Selesnick for the design of wavelet pairs for Kingsbury's dual-tree complex wavelet transform. Construction of two-dimensional (2-D) directional bases from tensor products of one-dimensional (1-D) phaselets is also described. Phaselets as a new approach to redundant wavelet transforms and their construction are both novel and should be interesting to the reader, independent of the approximate shift invariance property that this paper argues they possess.
Scott Shaobing Chen, Ramesh A. Gopinath
NeurIPS 2000
Peder A. Olsen, Ramesh A. Gopinath
ICASSP 2002
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ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Bing Xiang, Upendra V. Chaudhari, et al.
ICASSP 2002