  This paper proposes a deep learning-based method for generating 3D
microstructures from a single two-dimensional (2D) image, capable of producing
high-quality, realistic 3D images at low cost. In the method, a framework
(CEM3DMG) is designed to synthesize 3D images by learning microstructural
information from a 2D backscattered electron (BSE) image. Experimental results
show that CEM3DMG can generate realistic 3D images of arbitrary size with a
resolution of 0.47 $\mu m$ per pixel. Visual observation confirms that the
generated 3D images exhibit similar microstructural features to the 2D images,
including pores and particles morphology. Furthermore, quantitative analysis
reveals that these 3D microstructures closely match the real 2D microstructure
in terms of gray level histogram, phase proportions, and pore size
distribution.
