Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part I: Super-resolution enhancement using a 3D CNN

X-ray micro-computed tomography (micro-CT) is widely used for three-dimensional analysis of many rock types. However, the practical implementation of this method for micro-porous samples requires a compromise between the resolution of the images and the obtainable field of view (FOV). Generally, resolution enhancement results in a reduction of the FOV. The generation of high-quality micro-CT images is an expensive and time consuming task due to the competing requirements of a large FOV and fine resolution. To alleviate this, super-resolution processing, based on deep learning, is proposed to improve the quality of low-resolution images that can obtain a large FOV. In this research, a super-resolution technique employing the three-dimensional U-Net convolutional neural network (CNN) architecture was applied to enhance the resolution of granodiorite rock sample images. This was undertaken using two sets of micro-CT image triplexes, where the first triplex contained 3-, 6-, and 12-micron resolution sets, and the second triplex contained 1-, 2-, and 4-micron resolution sets. For each triplex, 80% of the images were used for training the neural network with the remaining 20% used for validation. Further validation was performed by comparing the processed results to images obtained from scanning electron microscopy (SEM). It was observed that super-resolution processing can significantly improve the low-resolution micro-CT image quality without physically reducing the sample size typically required for high-resolution scanning. It is expected that this technique could assist practitioners reveal features absent in small samples (e.g. large fractures and or rock textures). Furthermore, images restored through super-resolution processing maintain the FOV of the lower resolution scan, a task that would be comparatively expensive and time consuming to acquire in a high-resolution scan. The workflow proposed in this study has a significant impact on a range of fields including the numerical prediction of rock permeability, and segmentation for advanced mineral analysis.