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

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.