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  2. Daubechies wavelet - Wikipedia

    en.wikipedia.org/wiki/Daubechies_wavelet

    The Daubechies wavelets are not defined in terms of the resulting scaling and wavelet functions; in fact, they are not possible to write down in closed form. The graphs below are generated using the cascade algorithm, a numeric technique consisting of inverse-transforming [1 0 0 0 0 ... ] an appropriate number of times.

  3. Wavelet - Wikipedia

    en.wikipedia.org/wiki/Wavelet

    This subspace in turn is in most situations generated by the shifts of one generating function ψ in L 2 (R), the mother wavelet. For the example of the scale one frequency band [1, 2] this function is = ⁡ ⁡ = ⁡ ⁡ with the (normalized) sinc function. That, Meyer's, and two other examples of mother wavelets are:

  4. Lifting scheme - Wikipedia

    en.wikipedia.org/wiki/Lifting_scheme

    The predict step calculates the wavelet function in the wavelet transform. This is a high-pass filter. The update step calculates the scaling function, which results in a smoother version of the data. As mentioned above, the lifting scheme is an alternative technique for performing the DWT using biorthogonal wavelets.

  5. Scaling function - Wikipedia

    en.wikipedia.org/wiki/Scaling_function

    Download QR code; Print/export ... move to sidebar hide. Scaling function may refer to: Critical exponent § Scaling functions ... Wavelet § Scaling function

  6. Ricker wavelet - Wikipedia

    en.wikipedia.org/wiki/Ricker_wavelet

    is the negative normalized second derivative of a Gaussian function, i.e., up to scale and normalization, the second Hermite function. It is a special case of the family of continuous wavelets (wavelets used in a continuous wavelet transform) known as Hermitian wavelets. The Ricker wavelet is frequently employed to model seismic data, and as a ...

  7. Cascade algorithm - Wikipedia

    en.wikipedia.org/wiki/Cascade_algorithm

    In the mathematical topic of wavelet theory, the cascade algorithm is a numerical method for calculating function values of the basic scaling and wavelet functions of a discrete wavelet transform using an iterative algorithm. It starts from values on a coarse sequence of sampling points and produces values for successively more densely spaced ...

  8. Shannon wavelet - Wikipedia

    en.wikipedia.org/wiki/Shannon_wavelet

    In functional analysis, the Shannon wavelet (or sinc wavelets) is a decomposition that is defined by signal analysis by ideal bandpass filters. Shannon wavelet may be either of real or complex type. Shannon wavelet is not well-localized (noncompact) in the time domain, but its Fourier transform is band-limited (compact support).

  9. Diffusion wavelets - Wikipedia

    en.wikipedia.org/wiki/Diffusion_wavelets

    This algorithm constructs the scaling basis functions and the wavelet basis functions along with the representations of the diffusion operator at these scales. In the algorithm below, the subscript notation Φ a {\displaystyle \Phi _{a}} and Ψ b {\displaystyle \Psi _{b}} represents the scaling basis functions at scale a {\displaystyle a} and ...

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