2D Texture Image Synthesis of Cement

Cement, as an important material, is widely applied in the world. The macroscopic properties of cement are strongly related to its microstructure. To acquire cement microstructure images, scanning electron microscopy (SEM) is a popular scanning technique for obtaining high-quality backscattered electrons (BSE) images of cement microstructure. Nevertheless, this technique generally cannot acquire large-scale microstructural images due to the limited scanning area. Therefore, this paper proposes a BSE texture synthesis framework based on convolutional neural network (CNN) technology. In this framework, the generator can generate BSE images. The framework also optimizes the generator by perceptual loss. Ultimately, experiments demonstrate that our method can generate arbitrary size BSE textures similar to a given BSE texture.