  Available super-resolution techniques for 3D images are either
computationally inefficient prior-knowledge-based iterative techniques or deep
learning methods which require a large database of known low- and
high-resolution image pairs. A recently introduced tensor-factorization-based
approach offers a fast solution without the use of known image pairs or strict
prior assumptions. In this article this factorization framework is investigated
for single image resolution enhancement with an off-line estimate of the system
point spread function. The technique is applied to 3D cone beam computed
tomography for dental image resolution enhancement. To demonstrate the
efficiency of our method, it is compared to a recent state-of-the-art iterative
technique using low-rank and total variation regularizations. In contrast to
this comparative technique, the proposed reconstruction technique gives a
2-order-of-magnitude improvement in running time -- 2 minutes compared to 2
hours for a dental volume of 282$\times$266$\times$392 voxels. Furthermore, it
also offers slightly improved quantitative results (peak signal-to-noise ratio,
segmentation quality). Another advantage of the presented technique is the low
number of hyperparameters. As demonstrated in this paper, the framework is not
sensitive to small changes of its parameters, proposing an ease of use.
