Wolfram Language & System 11.0 (2016)|Legacy Documentation

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applies a bilateral filter of spatial spread σ and pixel value spread μ to image.

applies a bilateral filter to an array of data.

Details and OptionsDetails and Options

  • BilateralFilter is a nonlinear local filter used for edge-preserving smoothing.
  • BilateralFilter replaces each pixel by a weighted average of its neighbors, using normalized Gaussian matrices as weights.
  • BilateralFilter uses a Gaussian matrix of spatial radius 5/2 σ.
  • BilateralFilter works with arbitrary 2D and 3D images, as well as data arrays of any rank.
  • When applied to multichannel images, the Euclidean distance between channel vectors is computed.
  • BilateralFilter always returns an image of a real type.
  • For large values of μ, bilateral filtering yields results similar to Gaussian filtering.
  • BilateralFilter can take the following options:
  • MaxIterations1maximum number of iterations
    WorkingPrecisionMachinePrecisionthe precision to use

Background & Context
Background & Context

  • BilateralFilter is a filter for smoothing images to remove local variations typically caused by noise, rough textures, etc. BilateralFilter is often used as a preprocessing step before doing other image analysis operations, such as segmentation. Bilateral filtering can also be used to perform unsharp masking by subtracting the filtered image from the original and then adding the original back in.
  • BilateralFilter performs a nonlinear edge-preserving smoothing. The smoothing is done by replacing each pixel with a weighted average of its neighbors, where weights are taken from a normalized Gaussian distribution based on color value similarity. Here, the standard deviation σ and mean μ of the Gaussian distribution are specified as arguments.
  • BilateralFilter works with arbitrary grayscale and color images, as well as with 3D and 2D images. When applied to multichannel images, BilateralFilter does not operate channel-by-channel but rather uses the Euclidean distance between channel vectors.
  • Other edge-preserving filters include MeanShiftFilter and PeronaMalikFilter. Similar filters that are not edge preserving include MeanFilter and GaussianFilter. For large values of the Gaussian distribution mean, bilateral filtering yields results similar to Gaussian filtering.
Introduced in 2010
| Updated in 2012