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LBitmap::AnisotropicDiffusion

L_INT LBitmap::AnisotropicDiffusion(uIterations, uSmoothing, fTimeStep, fMinVariation, fMaxVariation, fEdgeHeight, uUpdate, uFlags)

L_UINT uIterations;	/* number of iterations */
L_UINT uSmoothing;	/* smoothing kernel size */
L_FLOAT fTimeStep;	/* time control over vector field distance */
L_FLOAT fMinVariation;	/* amount of parallel diffusion */
L_FLOAT fMaxVariation;	/* decrease of one sided diffusion */
L_FLOAT fEdgeHeight;	/* max edge height to be calculated */
L_UINT uUpdate;	/* number of iterations before each image update */
L_UINT32 uFlags;	/* flags */

Performs a 2-Dimensional Anisotropic Diffusion filter on an image for reduction of varied patterned and non-patterned noise

Parameter	Description
uIterations	Number of iterations the filter will perform. The size of the pyramid will be equal to this variable.
uSmoothing	Size of smoothing kernel which is performed on image.
fTimeStep	Defines the length of the vector field.
fMinVariation	Increasing this variable will increase the parallel diffusion.
fMaxVariation	Increasing this variable decreases the one sided diffusion.
fEdgeHeight	Max edge size which all edge values bellow will be included in the operation of the filter.
uUpdate	The number of iterations before each consecutive update of the image.
uFlags	Reserved for future use. Must be 0.

Returns

SUCCESS	The function was successful.
< 1	An error occurred. Refer to Return Codes.

Comments

This function is dedicated to reduction of noise in images, more specifically medical images. Images with a great amount of periodic noise in them are better suited to be clarified using this filter.

The number of iterations controls the number of times the filter will be performed on image. The lower the variable is, the faster the filter will perform. Higher iterations typically means clearer results, yet it depends on the maximum length of vectors that can be computed from the image to obtain the best results.

The higher the time step variable is, the larger of a range of vectors is used for the next iteration, yet too high a value, will yield out of range results as it will exceed the maximum vectors at some spots.

The variable uUpdate is very useful when using a large number of iterations on a large image, especially when the results are not known. This variable, updates the image every number Update iterations, thus the progress of the de-noising can be witnessed.

If the image has a region, the function will be performed on the region only.

This function supports 12 and 16-bit grayscale and 48 and 64-bit color images. Support for 12 and 16-bit grayscale and 48 and 64-bit color images is available in the Document and Medical Imaging toolkits.

This function does not support 32-bit grayscale images. It returns the error code ERROR_GRAY32_UNSUPPORTED if a 32-bit grayscale image is passed to this function.

Required DLLs and Libraries

LTIMGCOR For a listing of the exact DLLs and Libraries needed, based on the toolkit version, refer to Files To Be Included With Your Application.

Platforms

Win32, x64.

See Also

Functions:	LBitmap::SigmaFilter, LBitmap::TissueEqualize, LBitmap::MedianFilter, Class Members
Topics:	Raster Image Functions: Modifying Intensity Values
	Raster Image Functions: Processing an Image
	Removing Noise

Example

#define MAKE_IMAGE_PATH(pFileName) TEXT("C:\\Users\\Public\\Documents\\LEADTOOLS Images\\")pFileName
L_INT LBitmap__AnisotropicDiffusionExample()
{
   L_INT nRet;
   LBitmap LeadBitmap;
   nRet = LeadBitmap.Load(MAKE_IMAGE_PATH(TEXT("cannon.jpg")), 0,ORDER_BGR);
   if(nRet !=SUCCESS)
      return nRet;
   nRet = LeadBitmap.AnisotropicDiffusion(20, 1, 100.0f, 0.5f, 0.8f, 4.0f, 10, 0);
   if(nRet !=SUCCESS)
      return nRet;
   return SUCCESS;
}