  Digital Rock Imaging is constrained by detector hardware, and a trade-off
between the image field of view (FOV) and the image resolution must be made.
This can be compensated for with super resolution (SR) techniques that take a
wide FOV, low resolution (LR) image, and super resolve a high resolution (HR),
high FOV image. The Enhanced Deep Super Resolution Generative Adversarial
Network (EDSRGAN) is trained on the Deep Learning Digital Rock Super Resolution
Dataset, a diverse compilation 12000 of raw and processed uCT images. The
network shows comparable performance of 50% to 70% reduction in relative error
over bicubic interpolation. GAN performance in recovering texture shows
superior visual similarity compared to SRCNN and other methods. Difference maps
indicate that the SRCNN section of the SRGAN network recovers large scale edge
(grain boundaries) features while the GAN network regenerates perceptually
indistinguishable high frequency texture. Network performance is generalised
with augmentation, showing high adaptability to noise and blur. HR images are
fed into the network, generating HR-SR images to extrapolate network
performance to sub-resolution features present in the HR images themselves.
Results show that under-resolution features such as dissolved minerals and thin
fractures are regenerated despite the network operating outside of trained
specifications. Comparison with Scanning Electron Microscope images shows
details are consistent with the underlying geometry of the sample. Recovery of
textures benefits the characterisation of digital rocks with a high proportion
of under-resolution micro-porous features, such as carbonate and coal samples.
Images that are normally constrained by the mineralogy of the rock (coal), by
fast transient imaging (waterflooding), or by the energy of the source
(microporosity), can be super resolved accurately for further analysis
downstream.
