HilbertFilter

HilbertFilter[data, c]
applies a Hilbert filter with a cutoff frequency to an array of data.

HilbertFilter[data, c, n]
uses a filter kernel of length n.

HilbertFilter[data, c, n, wfun]
applies a smoothing window wfun to the filter kernel.

Details and OptionsDetails and Options

  • HilbertFilter works with arbitrary rank numerical arrays as well as with 3D and 2D images, operating separately on each channel.
  • When applied to images and multidimensional arrays, filtering is applied successively to each dimension, starting at level 1. HilbertFilter[data, {c1, c2, ...}] uses the frequency for the ^(th) dimension.
  • The cutoff frequency should be between 0 and .
  • HilbertFilter[data, c] uses a filter kernel length and smoothing window suitable for the cutoff frequency and the input data.
  • Typical smoothing windows include:
  • BlackmanWindowsmoothing with a Blackman window
    DirichletWindowno smoothing
    HammingWindowsmoothing with a Hamming window
    {v1,v2,...}use a window with values
    fcreate a window by sampling f over the range and
  • HilbertFilter takes a SampleRate option that specifies the sampling rate of the data. With SampleRate->r, the cutoff frequency should be between 0 and r×. »
  • By default, SampleRate->1 is assumed for images as well as data.
  • HilbertFilter works with SampledSoundList. With SampledSoundList[list, r], SampleRate->r is used.
  • For multichannel sound and image objects, each channel is processed separately.
  • HilbertFilter takes a Padding option that specifies the padding values to use on the sides of the input.
  • The default setting is Padding->"Fixed", corresponding to repetitions of the elements on each side.

ExamplesExamplesopen allclose all

Basic Examples (3)Basic Examples (3)

Hilbert transformer of a unit step sequence:

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Hilbert filtering of a sinusoidal sequence:

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Hilbert filtering of an image:

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