Improvement and Optimization of a 3D Reconstruction Algorithm for SEM Images of Porous Materials

Abstract

Porous materials have become increasingly common in people’s daily lives as the industry has advanced. Porous materials have numerous applications in the petroleum and chemical industries, as well as in everyday life. The study of diffusion, thermal conductivity, and percolation properties of porous materials has an important engineering application background and scientific value. The microstructure of materials affects their properties and attributes, so the description and visualization of the microstructure of porous materials is of great importance in the study of materials science. Due to the specificity of the internal structure of porous materials, many scenarios require 3-dimensional reconstruction of porous materials in practical engineering. In order to improve the effect of 3-dimensional reconstruction of porous materials, a 3D reconstruction method based on the improved generative adversarial neural network (GAN) is proposed in this paper for SEM images of porous materials. First, scanning electron microscope (SEM) images of porous materials are acquired, and then the acquired SEM images are preprocessed, including denoising and determining the boundary. Second, an improved GAN-based image super-resolution reconstruction model (ISRGAN) is used, and then the preprocessed images are fed into the ISRGAN model for training. Thus, multiple intermediate layer images are generated. Third, the 3D reconstruction of the intermediate layer images is performed using the slice combination method. The relationship between the unit cell pixels and the porosity is analyzed in the experiments to verify the effectiveness of the 3D reconstruction method used in this paper, and it is concluded that the porosity tends to be stable when the unit cell pixels converge to 110 and converge to the porosity of the real sample. The experimental results validate the feasibility and effectiveness of the method presented in this paper in the 3D reconstruction process.