SoBilateralFilterProcessing Class Reference
[Smoothing And Denoising]

SoBilateralFilterProcessing engine More...

#include <ImageViz/Engines/ImageFiltering/SmoothingAndDenoising/SoBilateralFilterProcessing.h>

Inheritance diagram for SoBilateralFilterProcessing:

Public Types
enum	FilterMode { BILATERAL = 0, SUSAN = 1 }
Public Member Functions
	SoBilateralFilterProcessing ()
Public Attributes
SoSFEnum	computeMode
SoSFVec3i32	kernelSize
SoSFImageDataAdapter	inImage
SoSFFloat	similarity
SoSFEnum	filterMode
SoImageVizEngineOutput < SoSFImageDataAdapter, SoImageDataAdapter * >	outImage

Detailed Description

SoBilateralFilterProcessing engine

The SoBilateralFilterProcessing engine edge-preserving smoothing filter.

For an introduction to smoothing and denoising image filters, see section Image Filters.

This engine is an edge-preserving smoothing filter. The intensity value at each voxel in an image is replaced by a weighted average of intensity values from nearby pixels. Crucially the weights depend on distance in color space from the voxel in question. This preserves sharp edges by systematically excluding pixels across discontinuities from consideration:

$O(i,j,k)=\frac{1}{K(i,j,k)}\sum_{l=-\frac{n_x}{2}}^{\frac{n_x}{2}}\sum_{m=-\frac{n_y}{2}}^{\frac{n_y}{2}}\sum_{n=-\frac{n_z}{2}}^{\frac{n_z}{2}} e^{_\frac{(I(i,j,k)-I(l,m,n))^2}{h^2}} I(l,m,n)$

where $h$ is a weighting similarity factor and $K$ is a local normalisation factor given by:

$K(i,j,k)=\sum_{l=-\frac{n_x}{2}}^{\frac{n_x}{2}}\sum_{m=-\frac{n_y}{2}}^{\frac{n_y}{2}}\sum_{n=-\frac{n_z}{2}}^{\frac{n_z}{2}} e^{_\frac{(I(i,j,k)-I(l,m,n))^2}{h^2}}$

The greater $h$ is, the stronger the blur is.

FILE FORMAT/DEFAULT

computeMode	MODE_AUTO
kernelSize	3 3 3
inImage	NULL
similarity	20.0f
filterMode	BILATERAL

Library references: bilateralfilter bilateralfilter3d

See related examples:: DenoisingImage3D, PoreAnalysis

Member Enumeration Documentation

enum SoBilateralFilterProcessing::FilterMode

Enumerator:

BILATERAL

SUSAN

This mode allows user switching this filter to the Susan filter (Smallest Univalue Segment Assimilating Nucleus).

Susan filter reproduces bilateral filter behavior simply excluding central voxel of computation. This filter gives better results in case of impulse noise.