On the Superiorization of Inversion Algorithms for Tomographic Absorption Spectroscopy

Superiorization, which introduces a priori conditions such as smoothness and sparseness to flame reconstruction, was applied to tomographic absorption spectroscopy (TAS) in this paper, as an improvement of existing tomographic inversion algorithms. Through simulation of TAS, the superiorization of algebraic reconstruction technique (ART) and maximum likelihood expectation maximization (MLEM) were studied. The performances of superiorized algorithms for different two-dimensional flame fields and under different target constraints were compared. The influence of noise on the reconstruction, as well as the computational efficiency of the superiorized algorithms under different conditions was studied. The results suggest that the superiorization can improve the tomographic inversion algorithms in terms of reconstruction accuracy and convergence rate.