  Existing medical image super-resolution methods rely on pairs of low- and
high- resolution images to learn a mapping in a fully supervised manner.
However, such image pairs are often not available in clinical practice. In this
paper, we address super-resolution problem in a real-world scenario using
unpaired data and synthesize linearly \textbf{eight times} higher resolved
Micro-CT images of temporal bone structure, which is embedded in the inner ear.
We explore cycle-consistency generative adversarial networks for
super-resolution task and equip the translation approach with Bayesian
inference. We further introduce \emph{Hu Moment distance} the evaluation metric
to quantify the shape of the temporal bone. We evaluate our method on a public
inner ear CT dataset and have seen both visual and quantitative improvement
over state-of-the-art deep-learning-based methods. In addition, we perform a
multi-rater visual evaluation experiment and find that trained experts
consistently rate the proposed method the highest quality scores among all
methods. Furthermore, we are able to quantify uncertainty in the unpaired
translation task and the uncertainty map can provide structural information of
the temporal bone.
