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Found 6 papers, 3 papers with code
DeePore: a deep learning workflow for rapid and comprehensive characterization of porous materials
DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images.
ML-LBM: Machine Learning Aided Flow Simulation in Porous Media
In the tortuous flow paths of porous media, Deep Learning techniques based on Convolutional Neural Networks (CNNs) are shown to give an accurate estimate of the steady state velocity fields (in all axes), and by extension, the macro-scale permeability.
Physical Accuracy of Deep Neural Networks for 2D and 3D Multi-Mineral Segmentation of Rock micro-CT Images
Segmentation of 3D micro-Computed Tomographic uCT) images of rock samples is essential for further Digital Rock Physics (DRP) analysis, however, conventional methods such as thresholding, watershed segmentation, and converging active contours are susceptible to user-bias.
Pixel Co-Occurence Based Loss Metrics for Super Resolution Texture Recovery
We find GLCM-based loss to result in images with higher pixelwise accuracy and better perceptual scores.
Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning
The network shows comparable performance of 50% to 70% reduction in relative error over bicubic interpolation.
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images
Single Image Super Resolution (SISR) techniques based on Super Resolution Convolutional Neural Networks (SRCNN) are applied to micro-computed tomography ({\mu}CT) images of sandstone and carbonate rocks.
