2026 Volume 17 Issue 3 Pages 751-769
As one of the applications of X-ray computed tomography (X-ray CT) to geomaterials, rock CT images have been widely applied in earth and environmental sciences. However, the rock CT images have a low-resolution problem in the depth direction due to multiple causes such as physical characteristics of the rock core samples, geometric constraints of the imaging environments, and limitations in measurement in X-ray CT scanners. In this study, we propose a data-driven super-resolution based on generative modeling to improve the depth resolution of the rock CT images. Our proposed method solves the low-resolution problem as conditional generation by latent diffusion models which are a class of generative models. Latent diffusion models enable high-fidelity data generation by learning the time-reversed stochastic dynamics of a non-equilibrium diffusion process in a learned latent space, where latent representations are progressively transformed into Gaussian noise. In the proposed method, when we assume three consecutive images at different depth levels, a second image (an unobservable rock CT image) is generated from a first image and a third image (observable rock CT images) based on the diffusion mechanism. We verify the effectiveness of the proposed method by using actual rock CT images obtained in Oman Drilling Project, which is one of the international scientific research projects. The experimental results suggest that our proposed method can estimate the unobservable rock CT images more accurately than existing interpolation methods in the qualitative evaluation. In particular, the quantitative assessment using mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) as evaluation metrics for images shows significant improvement of the values calculated from the metrics.
