Abstract
Anisotropic Non-Linear Diffusion is a powerful image processing technique, which allows to simultaneously remove the noise and enhance sharp features in two or three dimensional images. Anisotropic Diffusion is understood here in the sense of Weickert, meaning that diffusion tensors are anisotropic and reflect the local orientation of image features. This is in contrast with the non-linear diffusion filter of Perona and Malik, which only involves scalar diffusion coefficients, in other words isotropic diffusion tensors. In this paper, we present an anisotropic non-linear diffusion technique we implemented in ITK. This technique is based on a recent adaptive scheme making the diffusion stable and requiring limited numerical resources.
Highlights
Anisotropic Non-Linear Diffusion is a powerful image processing technique, which allows to simultaneously remove the noise and enhance sharp features in two or three dimensional images
Evidence is plentiful for Non-Linear Anisotropic Diffusion (NLAD) relevance in many other image processing applications, but its use has been limited by technical aspects so far
We describe in the following Coherence Enhancing Diffusion (CED) and Edge Enhancing Diffusion (EED), which are based on more complex tensor constructions introduced by Weickert [3]
Summary
Anisotropic Non-Linear Diffusion is a powerful image processing technique, which allows to simultaneously remove the noise and enhance sharp features in two or three dimensional images. Numerical schemes for LAD are in general non-trivial due to interaction between the anisotropic geometry of the diffusion tensors, and the cartesian structure of the pixel grid. In order to reduce user input, the diffusion tensors D can be defined in terms of the filtered image u.
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