Iterative Reconstruction of Coded Source Neutron Radiographs

A coded source facilitates high-resolution neutron imaging through magnification but requires that the radiographic data be deconvolved. A comparison of convolution-based and model-based de-blurring algorithms has been performed. Two convolution-based approaches are assessed, direct deconvolution and an iterative algorithm based on a maximum likelihood estimation (MLE)-like framework. The model-based approach specifies a geometric model of the neutron beam with a least squares formulation of the inverse imaging problem. Simulated data for both uniform and Gaussian shaped source distributions was used to study the impact of non-uniformities present in neutron beam distributions on the reconstructed images. Results indicate that the model based reconstruction method will match resolution and improve on contrast over convolution methods in the presence of non-uniform sources. Additionally, the model based iterative algorithm provides direct calculation of quantitative transmission values while the convolution based methods must be normalized based on known values.