Bayesian method with sparsity enforcing prior of dual-tree complex wavelet transform coefficients for X-ray CT image reconstruction

Abstract : In this paper, a Bayesian method with a hierarchical sparsity enforcing prior model for Dual-Tree Complex Wavelet Transform (DT-CWT) coefficients is proposed. This model is used for X-ray Computed Tomography (CT) image reconstruction. A generalized Student-t distributed prior model is used to enforce the sparse structure of the DT-CWT coefficient of the image. The joint Maximum A Posterior algorithm (JMAP) is used in this Bayesian context. Comparisons with the conventional and other state-of-the-art methods are presented, showing that the proposed method gives more accurate and robust reconstruction results while the dataset is insufficient.
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Communication dans un congrès
25th European Signal Processing Conference (EUSIPCO 2017), Aug 2017, Kos island, Greece. 〈10.23919/eusipco.2017.8081253 〉
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Soumis le : lundi 24 juillet 2017 - 16:06:27
Dernière modification le : dimanche 16 septembre 2018 - 22:06:01

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Li Wang, Ali Mohammad-Djafari, Nicolas Gac. Bayesian method with sparsity enforcing prior of dual-tree complex wavelet transform coefficients for X-ray CT image reconstruction. 25th European Signal Processing Conference (EUSIPCO 2017), Aug 2017, Kos island, Greece. 〈10.23919/eusipco.2017.8081253 〉. 〈hal-01567875〉

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