No title
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
