用生成模型将岩石3D微CT图像分辨率提升8倍，改善分割精度。
Super-Resolution of 3D Micro-CT Images Using Generative Adversarial Networks: Enhancing Resolution and Segmentation Accuracy
- 采用3D WGAN-GP模型，融合低分辨率3D微CT与高分辨率2D激光显微镜数据。
- 实现0.4375微米/体素的高分辨率图像，矿物与孔隙分割更准确。
- 适合数字岩心物理、地质建模等需要高精度三维图像的研究者。
我们开发了一种基于机器学习生成模型的方法，显著提升岩石3D微计算机断层扫描（micro-CT）图像的分割质量。该模型将分辨率提升八倍（8x），并解决因不同矿物和相态重叠X射线衰减导致的分割误差。所提模型为带梯度惩罚的3D深度卷积水张量生成对抗网络（3D DC WGAN-GP），在分割后的低分辨率3D micro-CT图像与未配对的互补2D高分辨率激光扫描显微镜（LSM）图像上训练。在多个贝雷亚砂岩样本上验证，获得分辨率达0.4375微米/体素的高质量超分辨率3D图像，并实现矿物组分与孔隙空间的精确分割。该方法可显著拓展现代数字岩心物理学的能力。
原文摘要 · Abstract (English)
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.
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