isotropic_diffusion — Perform an isotropic diffusion of an image.
The operator isotropic_diffusion performs an isotropic diffusion of the input image Image. This corresponds to a convolution of the image matrix with a Gaussian mask of standard deviation Sigma. If the parameter Iterations is set to 0, such a convolution is performed explicitly. For input images with a full ROI, isotropic_diffusion returns the same results as the operator derivate_gauss when choosing 'none' for its parameter Component. If the gray value matrix is larger than the ROI of Image the two operators differ since derivate_gauss takes the gray values outside of the ROI into account, while isotropic_diffusion mirrors the values at the boundary of the ROI in any case. The computational complexity increases linearly with the value of Sigma.
If Iterations has a positive value the smoothing process is considered as an application of the heat equation
For smaller values of Sigma, the convolution implementation is typically the faster method. Since the runtime of the partial differential equation solver only depends on the number of iterations and not on the value of Sigma, it is typically faster for large values of Sigma if few iterations are chosen (e.g., Iterations = 3).
For an explanation of the concept of smoothing filters see the introduction of chapter Filters / Smoothing.
Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.
Standard deviation of the Gauss distribution.
Default value: 1.0
Suggested values: 0.1, 0.5, 1.0, 3.0, 10.0, 20.0, 50.0
Restriction: Sigma > 0
Number of iterations.
Default value: 10
Suggested values: 0, 3, 10, 100, 500
Restriction: Iterations >= 0