Box-Constrained L1/L2 Minimization in Single-View Tomographic Reconstruction

We present a note on the implementation and efficacy of a box-constrained L1/L2 regularization in numerical optimization-based approaches to performing tomographic reconstruction from a single projection view. The constrained L1/L2 minimization problem is constructed and solved using the Alternating Direction Method of Multipliers (ADMM). We include brief discussions of parameter selection, as well as detailed numerical comparisons with relevant alternative methods. In particular, we benchmark against a box-constrained TVmin and an unconstrained Filtered Backprojection in both cone-beam and parallel-beam (Abel) forward models. We consider both a fully synthetic benchmark and reconstructions from X-ray radiographic image data.