Adaptive phase-retrieval stochastic reconstruction with correlation functions: Three-dimensional images from two-dimensional cuts,Physical Review E

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Adaptive phase-retrieval stochastic reconstruction with correlation functions: Three-dimensional images from two-dimensional cuts
Physical Review E ( IF 2.2 ) Pub Date : 2021-09-09 , DOI: 10.1103/physreve.104.035304
Aleksei Cherkasov ₁ , Andrey Ananev ₁ , Marina Karsanina ₂ , Aleksey Khlyupin ₁ , Kirill Gerke ₂

Affiliation

Precise characterization of three-dimensional (3D) heterogeneous media is indispensable in finding the relationships between structure and macroscopic physical properties (permeability, conductivity, and others). The most widely used experimental methods (electronic and optical microscopy) provide high-resolution bidimensional images of the samples of interest. However, 3D material inner microstructure registration is needed to apply numerous modeling tools. Numerous research areas search for cheap and robust methods to obtain the full 3D information about the structure of the studied sample from its 2D cuts. In this work, we develop an adaptive phase-retrieval stochastic reconstruction algorithm that can create 3D replicas from 2D original images, APR. The APR is free of artifacts characteristic of previously proposed phase-retrieval techniques. While based on a two-point

S_{2}

correlation function, any correlation function or other morphological metrics can be accounted for during the reconstruction, thus, paving the way to the hybridization of different reconstruction techniques. In this work, we use two-point probability and surface-surface functions for optimization. To test APR, we performed reconstructions for three binary porous media samples of different genesis: sandstone, carbonate, and ceramic. Based on computed permeability and connectivity (

C_{2}

and

L_{2}

correlation functions), we have shown that the proposed technique in terms of accuracy is comparable to the classic simulated annealing-based reconstruction method but is computationally very effective. Our findings open the possibility of utilizing APR to produce fast or crude replicas further polished by other reconstruction techniques such as simulated annealing or process-based methods. Improving the quality of reconstructions based on phase retrieval by adding additional metrics into the reconstruction procedure is possible for future work.

中文翻译：

具有相关函数的自适应相位检索随机重建：来自二维切割的三维图像

三维 (3D) 异质介质的精确表征对于发现结构与宏观物理特性（渗透率、电导率等）之间的关系是必不可少的。最广泛使用的实验方法（电子和光学显微镜）提供感兴趣样品的高分辨率二维图像。然而，应用众多建模工具需要 3D 材料内部微观结构配准。许多研究领域都在寻找廉价且稳健的方法，以从其 2D 切割中获取有关所研究样本结构的完整 3D 信息。在这项工作中，我们开发了一种自适应相位检索随机重建算法，可以从 2D 原始图像 APR 创建 3D 副本。APR 没有先前提出的相位检索技术所特有的伪影。

秒_{2}

相关函数、任何相关函数或其他形态指标都可以在重建过程中考虑在内，从而为不同重建技术的混合铺平了道路。在这项工作中，我们使用两点概率和表面-表面函数进行优化。为了测试 APR，我们对三种不同成因的二元多孔介质样品进行了重建：砂岩、碳酸盐和陶瓷。基于计算的渗透率和连通性（

C_{2}

和

升_{2}

相关函数），我们已经表明，所提出的技术在准确性方面与经典的基于模拟退火的重建方法相当，但在计算上非常有效。我们的研究结果开启了利用 APR 生产通过其他重建技术（如模拟退火或基于过程的方法）进一步抛光的快速或粗糙复制品的可能性。通过在重建过程中添加额外的指标来提高基于相位检索的重建质量是未来工作的可能。

更新日期：2021-09-10

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