The energy-minimization methods are based on the minimization of the
suitable designed objective, or energy function.
The process of inclusion an a prior information about the solution in to
the minimization is called regularization.
Regularized energy-minimization methods have applications in numerous
problems of different nature, such as segmentation,
image denoising and discrete tomography. It will be shown a
characteristic energy model for each of the considered problem.
We will discuss about the similarities and differences between these
models, but also about possible improvements
and applications in other problems.