bilateral_filterbilateral_filterBilateralFilterbilateral_filterBilateralFilterBilateralFilter (Operator)

Name

bilateral_filterbilateral_filterBilateralFilterbilateral_filterBilateralFilterBilateralFilter — bilateral filtering of an image.

Signature

Description

bilateral_filterbilateral_filterBilateralFilterbilateral_filterBilateralFilterBilateralFilter performs a joint bilateral filtering on the input ImageImageImageImageImageimage using the guidance image ImageJointImageJointImageJointImageJointImageJointimageJoint and returns the result in ImageBilateralImageBilateralImageBilateralImageBilateralImageBilateralimageBilateral. ImageImageImageImageImageimage and ImageJointImageJointImageJointImageJointImageJointimageJoint must be of the same size and type.

SigmaSpatialSigmaSpatialSigmaSpatialSigmaSpatialSigmaSpatialsigmaSpatial defines the size of the filter mask and corresponds to the standard deviation of a conventional gauss filter. Bigger values increase the area of influence of the filter and less detail is preserved.

SigmaRangeSigmaRangeSigmaRangeSigmaRangeSigmaRangesigmaRange is used to modify the filter mask depending on the pixels of ImageJointImageJointImageJointImageJointImageJointimageJoint around the current pixel. Only pixels in areas with weak edges that have a contrast lower than SigmaRangeSigmaRangeSigmaRangeSigmaRangeSigmaRangesigmaRange contribute to the smoothing. Please note that the contrast in uint2 or real images may differ significantly from the default values of SigmaRangeSigmaRangeSigmaRangeSigmaRangeSigmaRangesigmaRange and adjust the parameter accordingly.

GenParamNameGenParamNameGenParamNameGenParamNameGenParamNamegenParamName and GenParamValueGenParamValueGenParamValueGenParamValueGenParamValuegenParamValue currently can be used to control the trade-off between accuracy and speed (see below).

Influence of the Joint Image

Each pixel of ImageImageImageImageImageimage is filtered with a filter mask that depends on ImageJointImageJointImageJointImageJointImageJointimageJoint. The filter mask combines a Gaussian closeness function depending on SigmaSpatialSigmaSpatialSigmaSpatialSigmaSpatialSigmaSpatialsigmaSpatial and a Gaussian similarity function that weights gray value differences depending on SigmaRangeSigmaRangeSigmaRangeSigmaRangeSigmaRangesigmaRange.

If ImageImageImageImageImageimage and ImageJointImageJointImageJointImageJointImageJointimageJoint are identical, bilateral_filterbilateral_filterBilateralFilterbilateral_filterBilateralFilterBilateralFilter behaves like an edge-preserving smoothing where SigmaSpatialSigmaSpatialSigmaSpatialSigmaSpatialSigmaSpatialsigmaSpatial defines the size of the filter mask. Pixels at edges that have a contrast significantly greater than SigmaRangeSigmaRangeSigmaRangeSigmaRangeSigmaRangesigmaRange are preserved, while pixels in homogeneous areas are smoothed.

If ImageImageImageImageImageimage and ImageJointImageJointImageJointImageJointImageJointimageJoint are different, each pixel of ImageImageImageImageImageimage is smoothed with a filter mask that is influenced by ImageJointImageJointImageJointImageJointImageJointimageJoint. Pixels at positions where ImageJointImageJointImageJointImageJointImageJointimageJoint has strong edges with a contrast significantly greater than SigmaRangeSigmaRangeSigmaRangeSigmaRangeSigmaRangesigmaRange are smoothed less than pixels in homogeneous areas.

If ImageJointImageJointImageJointImageJointImageJointimageJoint is constant, bilateral_filterbilateral_filterBilateralFilterbilateral_filterBilateralFilterBilateralFilter is equivalent to a Gaussian smoothing with SigmaSpatialSigmaSpatialSigmaSpatialSigmaSpatialSigmaSpatialsigmaSpatial (see gauss_filtergauss_filterGaussFiltergauss_filterGaussFilterGaussFilter or smooth_imagesmooth_imageSmoothImagesmooth_imageSmoothImageSmoothImage).

Influence of the smoothing parameters

The following examples show the influence of SigmaRangeSigmaRangeSigmaRangeSigmaRangeSigmaRangesigmaRange on an artificial image. In this image, the noise level is 10 gray values, the left edge has a contrast of 50 gray values, the right edge has a contrast of 100 gray values. The yellow line shows a gray-value profile of a horizontal cross section.

Generic Parameters

The following values for GenParamNameGenParamNameGenParamNameGenParamNameGenParamNamegenParamName are supported:

'sampling_method'"sampling_method""sampling_method""sampling_method""sampling_method""sampling_method"

Per default, bilateral_filterbilateral_filterBilateralFilterbilateral_filterBilateralFilterBilateralFilter uses an approximation of the bilateral filter that only uses a subset of sampled points for the calculation of the local filter masks.

By setting 'sampling_method'"sampling_method""sampling_method""sampling_method""sampling_method""sampling_method", the used approximation can be selected. Possible values are:

'grid'"grid""grid""grid""grid""grid" (default)

Uses a regular grid for subsampling the filter masks.

'poisson_disk'"poisson_disk""poisson_disk""poisson_disk""poisson_disk""poisson_disk"

Uses a Poisson disk sampling. This method is slower than 'grid'"grid""grid""grid""grid""grid", but may produce less artifacts.

'exact'"exact""exact""exact""exact""exact"

Uses all available points. This method is slowest, but the most accurate. If 'exact'"exact""exact""exact""exact""exact" is used, 'sampling_ratio'"sampling_ratio""sampling_ratio""sampling_ratio""sampling_ratio""sampling_ratio" is ignored.

'sampling_ratio'"sampling_ratio""sampling_ratio""sampling_ratio""sampling_ratio""sampling_ratio"

Controls how many points are used for the subsampling of the local filter masks.

By setting 'sampling_ratio'"sampling_ratio""sampling_ratio""sampling_ratio""sampling_ratio""sampling_ratio" to 1.0, the exact method is used. Using a lower sampling ratio leads to faster filtering, but also to slightly less accurate results.

Suggested values: 0.25, 0.5, 0.75, 1.0

Default: 0.50

Rolling Bilateral Filter

bilateral_filterbilateral_filterBilateralFilterbilateral_filterBilateralFilterBilateralFilter can be applied iteratively. In this case, the result of one iteration is used as guidance image for the next iteration. This can be useful, e.g., to remove small structures from the original image even if they have a high contrast.

The following example shows the effect of a rolling filter on an artificial image. In this image, the noise level is 10 gray values, the contrast between dark and bright areas is 100 gray values, the left bright bar is 10 pixels wide, the right bar is 40 pixels wide. The yellow line shows a gray-value profile of a horizontal cross section. Used parameters: ImageJointImageJointImageJointImageJointImageJointimageJoint constant, SigmaSpatialSigmaSpatialSigmaSpatialSigmaSpatialSigmaSpatialsigmaSpatial = 25, SigmaRangeSigmaRangeSigmaRangeSigmaRangeSigmaRangesigmaRange = 15.

  * Apply the rolling bilateral filter
  * (use a constant guide for the first iteration).
  gen_image_proto (Image, ImageJoint, 128)
  for I := 1 to 6 by 1
    bilateral_filter (Image, ImageJoint, ImageJoint, 25, 15, [], [])
  endfor

Mathematical Background

The calculation of the filtered gray values is done on the basis of the following formula:

Parallelization

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Automatically parallelized on tuple level.
• Automatically parallelized on channel level.
• Automatically parallelized on domain level.

Parameters

Example (HDevelop)

read_image (Image, 'mreut')
* Edge-preserving smoothing
bilateral_filter (Image, Image, ImageBilateral, 5, 20, [], [])
* Rolling filter (5 iterations)
gen_image_proto (Image, ImageJoint, 0)
for I := 1 to 5 by 1
  bilateral_filter (Image, ImageJoint, ImageJoint, 5, 20, [], [])
endfor

Possible Predecessors

read_imageread_imageReadImageread_imageReadImageReadImage

Possible Successors

thresholdthresholdThresholdthresholdThresholdThreshold, dyn_thresholddyn_thresholdDynThresholddyn_thresholdDynThresholdDynThreshold, var_thresholdvar_thresholdVarThresholdvar_thresholdVarThresholdVarThreshold, regiongrowingregiongrowingRegiongrowingregiongrowingRegiongrowingRegiongrowing

Alternatives

guided_filterguided_filterGuidedFilterguided_filterGuidedFilterGuidedFilter, anisotropic_diffusionanisotropic_diffusionAnisotropicDiffusionanisotropic_diffusionAnisotropicDiffusionAnisotropicDiffusion, median_imagemedian_imageMedianImagemedian_imageMedianImageMedianImage

References

C. Tomasi, R. Manduchi: ”Bilateral filtering for gray and color images”; Sixth International Conference in Computer Vision; S. 839-846; January 1998.
F. Banterle, M. Corsini, P. Cignoni, R. Scopigno: ”A Low-Memory, Straightforward and Fast Bilateral Filter Through Subsampling in Spatial Domain”; Computer Graphics Forum, no. 1, vol 31; S. 19-23; February 2012.
G. Petschnigg, R. Szeliski, M. Agrawala, M. Cohen, H. Hoppe, K. Toyama: ”Digital Photography with Flash and No-flash Image Pairs”; ACM Trans., no. 3, vol. 23; S. 9; August 2004.
R. Bridson: ”Fast Poisson Disk Sampling in Arbitrary Dimensions”; ACM SIGGRAPH 2007 Sketches, no. 22; 2007.

Module

Foundation