A new resolution enhancement method for sandstone thin-section images using perceptual GAN

Abstract

The thin section is a sliver cut of rock sample and photographed by a microscope to help researchers investigate the mineral, structure, and geology details of a rock sample. For the purpose of photographing using the microsctope, only a small portion of the thin section can be visualized. The results, however, can hardly satisfy the requirement of a high resolution and larger scale visualization. The thin section is then trapped into a dilemma of contradiction between resolution and field of view. To solve this problem, we propose the Generative Adversarial Networks（GAN） based architectures representing the perceptual SR in this paper. Four perceptual super-resolution methods and two pixel-wise super-resolution methods are trained and tested for comparison with the dataset of sandstone thin-section. The perceptual GAN based super-resolution method is demonstrated to compensate for this limitation by enhancing the resolution of a large field of view image, which will provide a higher resolution detail recovery and larger field of view simultaneously. Based on the digital experiment analysis and comparison, thin-section perceptual loss with Residual-in-Residual Dense Block (RRDB) and Relativistic average GANs (RaGAN) shows a more realistic texture and evident edge for minerals and pores than other methods. With the new perceptual GAN based image enhancement method, the petroleum geologist can significantly improve the accuracy and reliability of all their thin section based research quality.