coherence_enhancing_diffcoherence_enhancing_diffCoherenceEnhancingDiffcoherence_enhancing_diffCoherenceEnhancingDiffCoherenceEnhancingDiff — Perform a coherence enhancing diffusion of an image.
The operator coherence_enhancing_diffcoherence_enhancing_diffCoherenceEnhancingDiffcoherence_enhancing_diffCoherenceEnhancingDiffCoherenceEnhancingDiff performs an
anisotropic diffusion process on the input image ImageImageImageImageImageimage to
increase the coherence of the image structures contained in
ImageImageImageImageImageimage. In particular, noncontinuous image edges are
connected by diffusion, without being smoothed perpendicular to
their dominating direction. For this,
coherence_enhancing_diffcoherence_enhancing_diffCoherenceEnhancingDiffcoherence_enhancing_diffCoherenceEnhancingDiffCoherenceEnhancingDiff uses the anisotropic diffusion
equation
formulated by Weickert. With a 2x2 coefficient
matrix G that depends on the gray values in ImageImageImageImageImageimage, this
is an enhancement of the mean curvature flow or intrinsic heat
equation
is constructed from the eigenvalues
and eigenvectors
of the resulting intermediate matrix,
where the functions
were determined empirically and taken from the publication of
Weickert.
Hence, the diffusion direction in mean_curvature_flowmean_curvature_flowMeanCurvatureFlowmean_curvature_flowMeanCurvatureFlowMeanCurvatureFlow is
only determined by the local direction of the gray value gradient,
while considers the macroscopic structure of
the image objects on the scale RhoRhoRhoRhoRhorho and the magnitude of the
diffusion in coherence_enhancing_diffcoherence_enhancing_diffCoherenceEnhancingDiffcoherence_enhancing_diffCoherenceEnhancingDiffCoherenceEnhancingDiff depends on how well
this structure is defined.
J. Weickert, V. Hlavac, R. Sara; “Multiscale texture
enhancement”; Computer analysis of images and patterns, Lecture
Notes in Computer Science, Vol. 970, pp. 230-237; Springer,
Berlin; 1995.
J. Weickert, B. ter Haar Romeny, L. Florack, J. Koenderink,
M. Viergever; “A review of nonlinear diffusion filtering”;
Scale-Space Theory in Computer Vision, Lecture Notes in
Comp. Science, Vol. 1252, pp. 3-28; Springer, Berlin; 1997.