anisotrope_diff — Smooth an image by edge-preserving anisotropic diffusion.
anisotrope_diff is obsolete and is only provided for reasons of backward compatibility. New applications should use anisotropic_diffusion instead.
The operator anisotrope_diff carries out an iterative, anisotropic smoothing process on the mathematical basis of physical diffusion. In analogy to the physical diffusion process describing the concentration balance between molecules dependent on the density gradient, the diffusion filter carries out a smoothing of the gray values dependent on the local gray value gradients.
For iterative calculation of the gray value of a pixel the gray value differences in relation to the four or eight neighbors, respectively, are used. These gray value differences, however, are evaluated differently, i.e., a non-linear diffusion process is carried out.
The evaluation is carried out by using a diffusion function (two different functions were implemented, namely Mode = 1 and/or 2), which --- depending on the gradient --- ensures that within homogenous regions the smoothing is stronger than over the margins of regions so that the edges remain sharp. The diffusion function is adjusted to the noise ratio of the image by a histogram analysis in the gradient image (according to Canny). A high value for Percent increases the smoothing effect but blurs the edges a little more (values from 80 - 90 percent are typical).
The parameter Iteration determines the number of iterations (typically 3--7).
Image to be smoothed.
For histogram analysis; higher values increase the smoothing effect, typically: 80-90.
Default value: 80
Suggested values: 65, 70, 75, 80, 85, 90
Typical range of values: 50 ≤ Percent ≤ 100
Minimum increment: 1
Recommended increment: 5
Selection of diffusion function.
Default value: 1
List of values: 1, 2
Number of iterations, typical values: 3-7.
Default value: 5
Suggested values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Typical range of values: 1 ≤ Iteration ≤ 30
Minimum increment: 1
Recommended increment: 1
Required neighborhood type.
Default value: 8
List of values: 4, 8
read_image(Image,'fabrik') anisotrope_diff(Image,Aniso,80,1,5,8) sub_image(Image,Aniso,Sub,2.0,127) disp_image(Sub,WindowHandle)
For each pixel: O(Iterations * 18).
If the parameter values are correct the operator anisotrope_diff returns the value 2 (H_MSG_TRUE). The behavior in case of empty input (no input images available) is set via the operator set_system('no_object_result',<Result>). If necessary an exception is raised.
regiongrowing, threshold, sub_image, dyn_threshold, auto_threshold
smooth_image, binomial_filter, gauss_image, sigma_image, rank_image, eliminate_min_max
P. Perona, J. Malik: “Scale-space and edge detection using anisotropic diffusion”, IEEE transaction on pattern analysis and machine intelligence, Vol. 12, No. 7, July 1990.