X-Rays Tomographic Reconstruction Images using Proximal Methods based on L 1 Norm and TV Regularization | Zendy

A. Allag | Zendy; Redouane Drai | Zendy; Abdessalem Benammar | Zendy; Tarek Boutkedjirt | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

X-Rays Tomographic Reconstruction Images using Proximal Methods based on L 1 Norm and TV Regularization

Author(s) -

A. Allag,

Redouane Drai,

Abdessalem Benammar,

Tarek Boutkedjirt

Publication year - 2018

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2018.01.119

Subject(s) - regularization (linguistics) , tomographic reconstruction , computer science , total variation denoising , inverse problem , algorithm , inverse , tomography , norm (philosophy) , iterative reconstruction , computer vision , artificial intelligence , image (mathematics) , mathematics , optics , physics , mathematical analysis , geometry , law , political science

In this paper, sparse regularization methods are applied to X-rays tomographic reconstruction 2D images. These methods are based on total variation algorithm associated to L1 norm and proximal functions. The inverse problem can therefore be regularized by using total variation regularisation based on proximal functions such as Forward-Backward, Douglas-Rachford and Chambolle-Pock approaches. We applied this method to non-destructive evaluation of material in the case of 2D reconstruction of X-rays tomographic images containing real defects.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research