Area of research
Computer Vision and Pattern Recognition · Mechanics of Materials
Research interest
Research topics from publications: An end-to-end three-dimensional reconstruction framework of porous media from a single two-dimensional image based on deep learning; Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks; Slice-to-voxel stochastic reconstructions on porous media with hybrid deep generative model. Representative work: Digital rock imaging plays an important role in studying the microstructure and macroscopic properties of rocks, where microcomputed tomography (MCT) is widely used. Due to the inherent limitations of MCT, a balance should be made between the field of view (FOV) and resolution of rock MCT images-a large FOV at low resolution (LR) or a small FOV at high resolution (HR). However, large FOV and HR are both expected for reliable analysis results in practice. Super-resolution (SR) is an effective solution to break through the mutual restriction between the FOV and resolution of rock MCT images, for it can reconstruct an HR image from a LR observation. Most of the existing SR methods cannot produc