Deep learning assisted denoising of fast polychromatic X-ray micro-CT imaging of multiphase flow in porous media

Understanding the flow of fluids in the subsurface and their interaction with different solid surfaces is crucial for addressing challenging geological applications such as CO₂ sequestration, enhanced oil recovery, and environmental remediation of polluted aquifers. Synchrotron-based 3D X-ray micro-computed tomography (micro-CT) has enabled the visualization of dynamic pore-filling events in multiphase flow experiments at sub-second time resolutions. However, the limited accessibility of synchrotron facilities has driven the use of low-flux polychromatic micro-CT systems, which often produce relatively noisy images during fast scans. To overcome this limitation, we propose a deep learning workflow using a cycleGAN network trained on unpaired datasets as no direct pixel-wise correspondence exists between the noisy domain and the high-quality domain. This approach transforms noisy fast polychromatic micro-CT scans into high-quality images, enabling detailed analysis of multiphase flow dynamics. The effectiveness of the denoising process was verified using blind image quality evaluators and Minkowski functionals for the non-wetting phases. The results indicate that the cycleGAN network achieves an average 1 to 6 percentage error difference for 3D morphological analysis parameters and outperforms other filtering methods such as non-local means and the adaptive Weiner filter, demonstrating its potential as a reliable technique for restoring noisy fast scans from polychromatic micro-CT systems.