Wavelet Filtering

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Wavelet Filtering

The Wavelet tool performs de-noising of dynamic data, whereby the x and y image dimensions must be a power of 2. Note that if the z and the time domain sizes are not a power of 2, they are extended or reduced to the closest 2n value.

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Wavelet filtering has been adapted from [1] and can be applied with two sets of coefficients:

Daubechies using 20 coefficients [1];

Battle-Lemarie using 71 coefficients obtained from Narayan Kovvali, at that time working at Duke University.

There are four different filtering modes:

each plane separately (2D),

each frame separately (3D),

each plane over time separately (2D + time),

or the entire volume over time (3D + time).

The coefficients to keep determine the level of smoothing: the lower the % value entered, the smoother the image.

Reference

1.William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. 1992. Numerical Recipes in C (2nd Ed.): The Art of Scientific Computing. Cambridge University Press, New York, NY, USA.