XRCT image processing for sand fabric reconstruction

We explore computationally efficient techniques to improve the XRCT image processing of low resolution and very noisy images for use in reconstruction of the fabric of densely packed, natural sand deposits. To this end we evaluate an image preprocessing workflow that incorporates image denoising, single image super resolution, image segmentation and level-set (LS) reconstruction. We show that, although computationally intensive, the Non-Local Mean (NLM) filter improves the quality of XRCT images of granular material by increasing the signal-to-noise ratio without impairing visible structures in the images, and outperforms more traditional local filters. We then explore an image super-resolution technique based on sparse signal representation and show that it performs well with noisy data and improves the subsequent stage of binarization. The image binarization is performed using a Hidden Markov Random Fields (HMRF) with Weighted Expectation Maximization (WEM) algorithm which takes the spatial information into account and performs well on high resolution images, however it still struggles with low quality images. We then use the level set method to define the grain geometry and show that the Distance Regularized LS Evolution (DRLSE) is an efficient approach for data sets with large numbers of grains. Finally, we introduce a penalty term into the evolution of the LS function, to address the issue of adhesion of much finer particles, such as clay, on the surface of the reconstructed avatars, while maintaining the main morphological details of the grains.