Name
Abstract | ||
|---|---|---|
We define a new wavelet transform that is based on a previously defined family of scaling functions: the fractional B-splines. The interest of this family is that they interpolate between the integer degrees of polynomial B-splines and that they allow a fractional order of approximation. The orthogonal fractional spline wavelets essentially behave as fractional differentiators. This property seems promising for the analysis of 1/f/sup /spl alpha// noise that can be whitened by an appropriate choice of the degree of the spline transform. We present a practical FFT-based algorithm for the implementation of these fractional wavelet transforms, and give some examples of processing. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/ICASSP.2000.862030 | ICASSP |
Keywords | Field | DocType |
fractional b-splines,polynomial b-splines,practical fft-based algorithm,new wavelet,fractional order,definition end implementation,orthogonal fractional spline,fractional differentiators,appropriate choice,fractional wavelet,integer degree,signal analysis,biomedical imaging,wavelet transform,polynomials,interpolate,spline,interpolation,wavelet transforms,white noise,iterative methods,signal detection,wavelet analysis,signal processing,image reconstruction,fast fourier transforms | Spline (mathematics),Mathematical optimization,Spline wavelet,Fractional wavelet transform,Continuous wavelet transform,Discrete wavelet transform,Fractional calculus,Mathematics,Wavelet,Wavelet transform | Conference |
ISBN | Citations | PageRank |
0-7803-6293-4 | 22 | 2.56 |
References | Authors | |
1 | 2 | |