The reconstruction of 3D microstructures from 2D slices is considered to hold
significant value in predicting the spatial structure and physical properties
of materials.The dimensional extension from 2D to 3D is viewed as a highly
challenging inverse problem from the current technological
perspective.Recently,methods based on generative adversarial networks have
garnered widespread attention.However,they are still hampered by numerous
limitations,including oversimplified models,a requirement for a substantial
number of training samples,and difficulties in achieving model convergence
during training.In light of this,a novel generative model that integrates the
multiscale properties of U-net with and the generative capabilities of GAN has
been proposed.Based on this,the innovative construction of a multi-scale
channel aggregation module,a multi-scale hierarchical feature aggregation
module and a convolutional block attention mechanism can better capture the
properties of the material microstructure and extract the image information.The
model's accuracy is further improved by combining the image regularization loss
with the Wasserstein distance loss.In addition,this study utilizes the
anisotropy index to accurately distinguish the nature of the image,which can
clearly determine the isotropy and anisotropy of the image.It is also the first
time that the generation quality of material samples from different domains is
evaluated and the performance of the model itself is compared.The experimental
results demonstrate that the present model not only shows a very high
similarity between the generated 3D structures and real samples but is also
highly consistent with real data in terms of statistical data analysis.

该研究提出了一种集成多尺度特性和生成对抗网络的创新生成模型，用于重建三维微结构，提高模型的准确性，并通过结合图像正则化损失和 Wasserstein 距离损失进一步改进模型，实现了生成的三维结构与真实样本的高相似性和统计数据一致性。