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Communication Dans Un Congrès Année : 2021

Efficiency of TV-regularized algorithms in computed tomography with Poisson-Gaussian noise

Résumé

Regularized algorithms are the state-of-the-art in computed tomography, but they are also very demanding in computer resources. In this work we test two data-fidelity formulations and some associated algorithms for the resolution of the Total-Variation regularized tomographic problem. We compare their computational cost for a mixture of Poisson and Gaussian noises. We show that a recently proposed MAP-EM algorithm outperforms the TV-regularized SIRT and the Chambolle-Pock algorithms on synthetic data for the considered noise. We illustrate this result on experimental data from transmission electron microscopy.
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Dates et versions

hal-03447989 , version 1 (24-11-2021)

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Theo Leuliet, Louise Friot–giroux, Walid Baaziz, Elie Bretin, Ovidiu Ersen, et al.. Efficiency of TV-regularized algorithms in computed tomography with Poisson-Gaussian noise. 2020 28th European Signal Processing Conference (EUSIPCO), Jan 2021, Amsterdam, Netherlands. pp.1294-1298, ⟨10.23919/Eusipco47968.2020.9287762⟩. ⟨hal-03447989⟩
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