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Communication Dans Un Congrès Année : 2017

3D X-ray Computed Tomography reconstruction using sparsity enforcing Hierarchical Model based on Haar Transformation

Li Wang
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Nicolas Gac

Résumé

In this paper, we consider the 3D X-ray CT reconstruction problem by using the Bayesian approach with a hierarchical prior model. A generalized Student-t distributed prior model is used to enforce the sparse structure of the multilevel Haar Transformation of the image. Comparisons with some state of the art methods are presented, showing that the proposed method gives more accurate reconstruction results and a faster convergence. Simulation results are also provided to show the effectiveness of the proposed hierarchical model for a reconstruction with more limited projections.
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Dates et versions

hal-01490554 , version 1 (15-03-2017)

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  • HAL Id : hal-01490554 , version 1

Citer

Li Wang, Ali Mohammad-Djafari, Nicolas Gac, Mircea Dumitru. 3D X-ray Computed Tomography reconstruction using sparsity enforcing Hierarchical Model based on Haar Transformation. The 2017 International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Jun 2017, Xi'an, China. pp.295-298. ⟨hal-01490554⟩
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