  We present a note on the implementation and efficacy of a box-constrained
$L_1/L_2$ regularization in numerical optimization approaches to performing
tomographic reconstruction from a single projection view. The constrained
$L_1/L_2$ minimization problem is constructed and solved using the Alternating
Direction Method of Multipliers (ADMM). We include brief discussions on
parameter selection and numerical convergence, as well as detailed numerical
demonstrations against relevant alternative methods. In particular, we
benchmark against a box-constrained TVmin and an unconstrained Filtered
Backprojection in both cone and parallel beam (Abel) forward models. We
consider both a fully synthetic benchmark, and reconstructions from X-ray
radiographic image data.
