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Discrete Wavelet Transforms

Authors: Jean-Pierre Antoine; Syed Twareque Ali; Jean-Pierre Gazeau;

Discrete Wavelet Transforms

Abstract

As we have seen in Section 12.5, the discretization of the CWT leads, among other things, to the theory of frames. For many practical purposes of signal processing, a tight frame is almost as good as an orthonormal basis. Actually, if one stays with the standard wavelets, as we have done so far, one cannot do better, since these wavelets do not generate any orthonormal basis (like the usual coherent states). There are cases, however, in which an orthonormal basis is really required. A typical example is data compression, which is performed (in the simplest case) by removing all wavelet expansion coefficients below a fixed threshhold. In order to not introduce any bias in this operation, the coefficients have to be as decorrelated as possible, and, of course, an orthonormal basis is ideal in this respect.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Average
Top 10%
Average