Quality enhancement of time-resolved computed tomography scans with cycleGAN

Abstract: Time-resolved x-ray tomography enables us to dynamically and non-destructively study the interior of a specimen. The obtainable temporal resolution is limited by the x-ray flux and the desired spatial resolution. To allow faster acquisition speeds, we explore a deep-learning approach that applies super-resolution and image denoising to fast time-resolved tomograms. The domain translation algorithm, cycleGAN, can apply the high image quality of slow-acquisition tomograms to low-quality fast-acquisition tomograms. It can be trained with unpaired datasets, enabling different samples and detector setups for recording the fast-acquisition and slow-acquisition datasets. In this thesis, we use time-resolved tomograms of carbon microfibers to evaluate the cycleGAN algorithm. The aim is to retrieve ultra-fast, high-quality tomograms that permit detailed studies of carbon fibers.