Difference between revisions of "Filters (Wavelet): De-noising BLS GSM"
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| − | + | == Background == | |
| + | The wavelet transform or wavelet analysis is a solution to overcome the shortcomings of the Fourier transform. In wavelet analysis, the modulated window is shifted along the signal, and for every position, the spectrum is calculated. Then, this process is repeated many times with a slightly shorter (or longer) window for every new cycle. In the end, the result appears as a collection of time-frequency representations of the signal, all with different resolutions. Since the modulated window is fully scalable, this solves the signal-cutting problem which arises in the Fourier transform. | ||
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| + | Because the result appears as a collection of time-frequency representations of the signal, we can speak of a multiresolution analysis. However, in the case of wavelets, we normally do not speak about time-frequency representations, but about time-scale representations, scale being in a way the opposite of frequency, because the term frequency is reserved for the Fourier transform. | ||
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| + | For more information, refer to [http://mipav.cit.nih.gov/documentation/HTML%20Algorithms/FiltersWaveletDenoisingBLSGSM.html the MIPAV HTML Help page]. | ||
[[Category:Help:Stub]] | [[Category:Help:Stub]] | ||
[[Category:Help]] | [[Category:Help]] | ||
[[Category:Help:Algorithms]] | [[Category:Help:Algorithms]] | ||
Revision as of 18:52, 29 January 2013
This page is a stub.
Background
The wavelet transform or wavelet analysis is a solution to overcome the shortcomings of the Fourier transform. In wavelet analysis, the modulated window is shifted along the signal, and for every position, the spectrum is calculated. Then, this process is repeated many times with a slightly shorter (or longer) window for every new cycle. In the end, the result appears as a collection of time-frequency representations of the signal, all with different resolutions. Since the modulated window is fully scalable, this solves the signal-cutting problem which arises in the Fourier transform.
Because the result appears as a collection of time-frequency representations of the signal, we can speak of a multiresolution analysis. However, in the case of wavelets, we normally do not speak about time-frequency representations, but about time-scale representations, scale being in a way the opposite of frequency, because the term frequency is reserved for the Fourier transform.
For more information, refer to the MIPAV HTML Help page.
