Optimisation methods for tomography
1 : Université Paris-Est ESIEE / LIGM
(LIGM)
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Website
Fédération de Recherche Bézout, École des Ponts ParisTech (ENPC), ESIEE, Université Paris-Est Marne-la-Vallée (UPEMLV), CNRS : UMR8049
ESIEE Paris, 2 boulevard Blaise-Pascal, 93162 Noisy-le-Grand Cedex -
France
In this talk we will introduce optimization methods for tomographic reconstruction. In this talk we will cover the following topics:
- Motivation: tomography as an inverse problem
- Introduction to optimisation: Objective function; Constraints; Statistical interpretation; Maximum Likelihood (ML) and Maximum a Posteriori (MAP) models.
- Optimisation and tomography: Inverse problems formulation; Simple solutions: least squares; Filtered Back Projection as a ML formulation; Algebraic methods as a MAP formulation; Regularization functionals; sparsity; Total Variation.
- Applications: Joint denoising and tomography; Sparse reconstruction; Join segmentation and reconstruction; Local tomography.
- Software packages : Python, Matlab, C++, Fortran packages for optimization. Dedicated tomography packages.
| Subject : | : | Invited talk |
| Topics | : | Algorithmic and Material Developments |
| PDF version | : |  PDF version |
- Presentation
