Comparison of Algebraic Reconstruction Techniques and Maximum Likelihood-Expectation Maximization Tomographic Algorithms for Reconstruction of Gaussian Plume

The maximum likelihood-expectation maximization (ML-EM) and algebraic reconstruction techniques (ART) algorithm are two different iterative algorithms commonly used in the optical remote sensing tomography techniques. In this paper, the two algorithm are compared and analyzed on some evaluation parameters of reconstruction quality with the Gaussian plume model at C level of atmospheric stability as the simulation of gas diffusion. The experimental results show that in aspect of smoothness, peak shape and tailing peak position of reconstructed concentration distribution, ML-EM algorithm performs better. The ML-EM algorithm convergence, in terms of MSE, is much more rapid than that of ART algorithm. While in terms of PE, it becomes deteriorated compared to that of ART algorithm at slightly higher iterative numbers. This study is valuable in the search for optical remote sensing tomographic problems with limited projection data and fan-beam geometry.