标题： 基于生成对抗网络的3D显微CT图像超分辨率：提高分辨率和分割准确性 显示英文标题

标题： Super-Resolution of 3D Micro-CT Images Using Generative Adversarial Networks: Enhancing Resolution and Segmentation Accuracy

摘要： 我们开发了一种程序，用于显著提高岩石的分段三维微计算机断层扫描（micro-CT）图像的质量，该程序使用了机器学习（ML）生成模型。所提出的模型将分辨率提高了八倍（8x），并解决了由于微CT测量中不同岩石矿物和相的X射线衰减重叠导致的分割不准确问题。所提出的生成模型是一个具有梯度惩罚的三维深度卷积Wasserstein生成对抗网络（3D DC WGAN-GP）。该算法在分段的三维低分辨率micro-CT图像和分段的非配对互补二维高分辨率激光扫描显微镜（LSM）图像上进行训练。该算法已在Berea砂岩的多个样本上进行了演示。我们获得了高质量的超分辨率三维图像，分辨率为0.4375微米/体素，并对组成矿物和孔隙空间进行了准确分割。所述程序可以显著扩展数字岩石物理的现代能力。 显示英文摘要

摘要： We develop a procedure for substantially improving the quality of segmented 3D micro-Computed Tomography (micro-CT) images of rocks with a Machine Learning (ML) Generative Model. The proposed model enhances the resolution eightfold (8x) and addresses segmentation inaccuracies due to the overlapping X-ray attenuation in micro-CT measurement for different rock minerals and phases. The proposed generative model is a 3D Deep Convolutional Wasserstein Generative Adversarial Network with Gradient Penalty (3D DC WGAN-GP). The algorithm is trained on segmented 3D low-resolution micro-CT images and segmented unpaired complementary 2D high-resolution Laser Scanning Microscope (LSM) images. The algorithm was demonstrated on multiple samples of Berea sandstones. We achieved high-quality super-resolved 3D images with a resolution of 0.4375 micro-m/voxel and accurate segmentation for constituting minerals and pore space. The described procedure can significantly expand the modern capabilities of digital rock physics.