With the continuous improvement of mathematical modeling technology, reconstructing the three-dimensional structure of media from two-dimensional reference images has become an important research method for the three-dimensional modeling of multi-porous media. Deep-learning-based methods are currently popular and form the focus of this research field. However, the performance of deep learning in reconstructing the three-dimensional structure of media from two-dimensional reference images still requires improvement. To enhance the diversity and generalization of network-generated three-dimensional models, this study proposed a preprocessing method that correlated two-dimensional reference images with Gaussian noise, a three-orthogonal random section constraint method, and a dual generative adversarial network (DGAN)-based model. Multiple sets of core samples, a set of building materials, and a set of battery-material samples were used to verify the performance of the proposed network. Both intuitive morphological and statistical feature comparisons showed that the DGAN model solved the problem of insufficient diversity and generalization when reconstructing three-dimensional porous media from a single image using deep-learning-based methods. The morphological and statistical features of the reconstructed three-dimensional structure also exhibited good consistency with the reference two-dimensional image, and the training efficiency of the network was greatly improved.

中文翻译：

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使用双生成对抗网络提高 3D 多孔介质的重建性能


随着数学建模技术的不断改进，从二维参考图像重建介质的三维结构已成为多孔介质三维建模的重要研究方法。基于深度学习的方法目前很流行，并构成了该研究领域的重点。然而，深度学习在从二维参考图像重建媒体三维结构方面的性能仍有待改进。为了增强网络生成三维模型的多样性和泛化性，本研究提出了一种将二维参考图像与高斯噪声相关联的预处理方法、一种三正交随机截面约束方法和一种基于双生成对抗网络 （DGAN） 的模型。使用多组核心样本、一组建筑材料和一组电池材料样本来验证所提出的网络的性能。直观的形态学和统计特征比较都表明，DGAN 模型解决了使用基于深度学习的方法从单个图像重建三维多孔介质时多样性和泛化不足的问题。重建的三维结构的形态学和统计特征也表现出与参考二维图像的良好一致性，大大提高了网络的训练效率。