Depth super-resolution of rock CT images based on latent diffusion models by deep learning

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. 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) in our method. 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 demonstrate advantages in the performance of our method in both qualitative and quantitative aspects compared to conventional interpolation methods.