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(FG 3) Description: Understanding an image as binary grey ``alloy'' of a black and a white component, we use a nonlocal phase separation model [1], [3], [4] to describe image segmentation and noise reduction.
The model consists in a degenerate nonlinear parabolic equation with a nonlocal drift term additionally to the familiar Perona-Malik model:
![\begin{displaymath}
\frac{\partial u}{\partial t} -
\nabla \cdot [f(\vert\nabla ...
 ...abla w}{\phi ''(u)})]
+ \beta (u-g)=0, ~~
u(0,\cdot)= g(\cdot),\end{displaymath}](img517.gif)

 is a convex function, the kernel
 is a convex function, the kernel  represents nonlocal 
attracting forces, and v may be interpreted as chemical potential.
 represents nonlocal 
attracting forces, and v may be interpreted as chemical potential.
 and
 and  are given by
 are given by
 ,
,  .
.
 , k,
, k,  in the example) can be 
adjusted by minimizing a functional evaluating smoothness and entropy 
of u and the distance between u and g.
 in the example) can be 
adjusted by minimizing a functional evaluating smoothness and entropy 
of u and the distance between u and g. 
We formulate conditions for the model parameters to guarantee global existence of a unique solution that tends exponentially in time to a unique steady state. This steady state is solution of a nonlocal nonlinear elliptic boundary value problem and allows a variational characterization.
The application of the model to noise reduction is related to model parameters guaranteeing a unique steady state. Image segmentation is related to parameters where a unique steady state may not exist.
Figure 1 demonstrates some properties of the model applied to the noise reduction problem.
References:
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