Apprentissage de méthodes itératives pour l’imagerie de contraste de phase des rayons X - Archive ouverte HAL
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Poster

Apprentissage de méthodes itératives pour l’imagerie de contraste de phase des rayons X

Abstract : In-line phase contrast imaging can be obtained with X-rays if the beam is sufficiently coherent. Retrieving the phase and absorption from one or several of these images is a non-linear, ill-posed inverse problem, however. Here, we propose a deep learning method to solve this twofold problem from a single intensity measurement. It is based on the unrolling approach, in which an iterative algorithm is "unrolled" into a sequence, and some part of the iterations is replaced with a convolutional neural network. An advantage is that no regularization is needed, only the gradient of the data fidelity term is calculated at each iteration. The network is trained to learn a complete iteration from this information and the current iteration. We apply the method to a gradient descent scheme using smooth total variation regularization (GD-TVϵ) and compare the standard and the unrolling version (DUGD). We show that the unrolled version gives better results quantitatively and qualitatively, and that the computation time is much shorter than the classical iterative version.
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https://hal.archives-ouvertes.fr/hal-03706103
Contributeur : Béatrice Rayet Connectez-vous pour contacter le contributeur
Soumis le : lundi 27 juin 2022 - 14:23:06
Dernière modification le : samedi 24 septembre 2022 - 14:44:05

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  • HAL Id : hal-03706103, version 1

Citation

Kannara Mom, Max Langer, Bruno Sixou. Apprentissage de méthodes itératives pour l’imagerie de contraste de phase des rayons X. XXVIIIème Colloque Francophone de Traitement du Signal et des Images (Gretsi 2022), Sep 2022, Nancy, France. ⟨hal-03706103⟩

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