  X-ray micro-computed tomography (micro-CT) has been widely leveraged to
characterise pore-scale geometry in subsurface porous rock. Recent developments
in super resolution (SR) methods using deep learning allow the digital
enhancement of low resolution (LR) images over large spatial scales, creating
SR images comparable to the high resolution (HR) ground truth. This circumvents
traditional resolution and field-of-view trade-offs. An outstanding issue is
the use of paired (registered) LR and HR data, which is often required in the
training step of such methods but is difficult to obtain. In this work, we
rigorously compare two different state-of-the-art SR deep learning techniques,
using both paired and unpaired data, with like-for-like ground truth data. The
first approach requires paired images to train a convolutional neural network
(CNN) while the second approach uses unpaired images to train a generative
adversarial network (GAN). The two approaches are compared using a micro-CT
carbonate rock sample with complicated micro-porous textures. We implemented
various image based and numerical verifications and experimental validation to
quantitatively evaluate the physical accuracy and sensitivities of the two
methods. Our quantitative results show that unpaired GAN approach can
reconstruct super-resolution images as precise as paired CNN method, with
comparable training times and dataset requirement. This unlocks new
applications for micro-CT image enhancement using unpaired deep learning
methods; image registration is no longer needed during the data processing
stage. Decoupled images from data storage platforms can be exploited more
efficiently to train networks for SR digital rock applications. This opens up a
new pathway for various applications of multi-scale flow simulation in
heterogeneous porous media.
