Adaptive phase-retrieval stochastic reconstruction with correlation functions: 3D images from 2D cuts

Abstract: Precise characterization of three-dimensional 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 bi-dimensional 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 "full" 3D information about the structure of the studied sample from its 2D cuts. In this work, we develop a dynamic phase-retrieval stochastic reconstruction algorithm that can create 3D replicas from 2D original images - DDTF. The DDTF 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 DDTF, 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 DDTF to produce fast or crude replicas further polished by other reconstruction techniques such as simulated annealing or process-based methods.