Smooth an image by edge-preserving anisotropic diffusion.

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).

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 handling is raised.

anisotrope_diff is reentrant and automatically parallelized (on tuple level, channel level).

read_image, grab_image

regiongrowing, threshold, sub_image, dyn_threshold, auto_threshold

sigma_image, rank_image

smooth_image, 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.

Foundation