• Aucun résultat trouvé

Inverse problem resolution using sparseregularization method based on L

N/A
N/A
Protected

Academic year: 2021

Partager "Inverse problem resolution using sparseregularization method based on L"

Copied!
1
0
0

Texte intégral

(1)

The International Federation of Operational Research Societies (IFORS 2013 ) 2013

Inverse problem resolution using sparse regularization method based on L 1 Norm.

Application to X-rays tomographic reconstruction images

Aicha ALLAG, Redouane DRAI, T. BOUTEKDJIRT

Abstract : In this paper, a sparse regularization method in an orthogonal basis is studied and applied to X-rays tomographic reconstruction 2D images. This method is based on total variation algorithm associated to L1 norm. The inverse problem can therefore be regularized by using primal dual formulation based on proximal functions. We applied this method to non-destructive evaluation of material in the case of 2D reconstruction of X-rays tomographic images containing real defects

Keywords : radiography, X Ray, L1 norm, Tomography

Centre de Recherche en Technologies Industrielles - CRTI - www.crti.dz

Powered by TCPDF (www.tcpdf.org)

Références

Documents relatifs

The representation of the transfer point in a solution is introduced in Figure 10 The transfer point to leave clients is named with L i , while the transfer point to pick up

The user friendly windows-based program LLECMOD was used for the hydrodynamics and mass transfer simulation of RDC extraction columns for steady state simulation

The method proposed in our communication is based on a Conjugate Gradient algorithm which is an iterative regularization method which is relevant for minimizing

Total variation reg- ularization with Split Bregman based method in magnetic induction tomography using experimental data... Abstract- Magnetic induction tomography (MIT) is an imaging

In image processing, a segmentation is a process of partitioning an image into multiple sets of pixels, defined as super-pixels, in order to have a representation which is

This result is a straightforward consequence of Lemma 5.2 in the Appendix, where it is shown that the oracle in the class of binary filters λ i ∈ {0, 1} achieves the same rate

Hung Nguyen, “On the iterative regularization of inverse heat conduction problems by conjugate gradient method”, International Communications in Heat and Mass

В этом случае интерполируется положение вершины (Рис. Возможно, изо- значение совпадет со значением поля в узле сетки. В этом случае положение вершины