Diffusion (image processing)

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Diffusion is used in image processing to create what is known as scale space . In the Scale Space , the smoothing of an image depends on a freely adjustable scale parameter. The larger the scale parameter, the stronger the smoothing and the more small structures are suppressed in the image. The following figure illustrates the principle using a 1D image (line image).

Here the diffusion equations are used to simulate a diffusion process of the gray values ​​in the image. The scale variable ξ corresponds to the time t . It turns out that the (linear, isotropic) diffusion corresponds to a convolution with a Gaussian filter. This further emphasizes the smoothing property of diffusion.

Diffusion can therefore be used to suppress image disturbances or to emphasize certain image structures (by smoothing out smaller structures).

Edge-preserving diffusion filtering can be done by anisotropic diffusion. Here, the diffusion at the edges of the structures depends on the direction of the gradient. This presupposes an edge model, with the result that edges that do not follow the model nevertheless disappear due to the smoothing. It is therefore useful for the method to bring model knowledge into the edge model.