The fracture network of a porous media image is interesting for a variety of reasons. It is essential for estimating material strength, elasticity, fluid interactions, and other metrics. In this thesis, 3D image data gathered by an X-ray CT scan of porous marble is denoised, and super resolution images are created. The latter was done because cracks in the fracture network are often of sub-pixel width, therefore needing a super resolution to be depicted accurately. Increasing the resolution of the X-ray CT scan itself is very costly, whereas using image processing to estimate higher-resolution images of similar quality is far cheaper. To create denoised and super resolution images, two computer vision machine learning models based on the U-Net architecture were examined. These types of models have been improving rapidly and have proven capable for many image processing tasks. While other fields, for example, medical imaging, already use 3D image segmentation extensively, combining the analysis of porous media imagery with U-Net models operating on a 3D input space has not been done before. To achieve the best possible results, both model parameters were optimized to fit this specific task. This evaluation of the resulting images shows promising results, yielding highly accurate super resolution images using this machine learning approach. Keywords: U-Net, XRCT, super resolution, porosity, machine learning