1 code implementation • 3 May 2020 • Arash Rabbani, Masoud Babaei, Reza Shams, Ying Da Wang, Traiwit Chung
DeePore is a deep learning workflow for rapid estimation of a wide range of porous material properties based on the binarized micro-tomography images.
1 code implementation • 22 Apr 2020 • Ying Da Wang, Traiwit Chung, Ryan T. Armstrong, Peyman Mostaghimi
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.
1 code implementation • 13 Feb 2020 • Ying Da Wang, Mehdi Shabaninejad, Ryan T. Armstrong, Peyman Mostaghimi
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.
no code implementations • 25 Sep 2019 • Ying Da Wang, Pawel Swietojanski, Ryan T Armstrong, Peyman Mostaghimi
We find GLCM-based loss to result in images with higher pixelwise accuracy and better perceptual scores.
no code implementations • 15 Jul 2019 • Ying Da Wang, Ryan T. Armstrong, Peyman Mostaghimi
The network shows comparable performance of 50% to 70% reduction in relative error over bicubic interpolation.
no code implementations • 16 Apr 2019 • Ying Da Wang, Ryan Armstrong, Peyman Mostaghimi
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.