Comparison of heuristic and deterministic algorithms in neutron coded imaging reconstruction

Abstract

Neutron coded imaging is an effective tool for diagnosing the shape, size and symmetry of deuterium (D)–tritium (T) plasma in inertial confinement fusion (ICF). It can provide an important reference for designing and improving the D–T pellet and confinement configuration. Image reconstruction algorithms play a role of reconstructing the source images from the blurred coded images and the point spread functions (PSFs) of imaging systems. Conventionally, the convolution model is used as the mathematical model for neutron coded imaging reconstruction, but it applies only to the spatially invariant PSF. In this paper, the linear equations model is regarded as the mathematical model for the reconstruction, and it can also be suitable for spatially variant PSF. In the reconstruction, the spatially variant PSFs were simulated through Monte Carlo method. Then an improved genetic algorithm (IGA) for the source image reconstruction was proposed. The comparison of its performance with other types of deterministic algorithms (like the algorithm with total variation (TV) minimization) was conducted, and the results showed that the IGA has better performance in source reconstruction regardless of the utilization of TV sparse prior.