Deep-neural-network denoising of diffraction data

University of Zurich

Removal or cancellation of noise has wide-spread applications for imaging and acoustics. In every-day-life applications – such as image restoration – denoising may even include generative aspects, which are unfaithful to the ground truth. For scientific use, however, denoising must reproduce the ground truth accurately. Denoising scientific data is further challenged by unknown noise profiles. In fact, such data will often include noise from multiple distinct sources, which significantly reduces the applicability of simulation-based approaches. We show how scientific data can be denoised via a deep convolutional neural network such that weak signals appear with quantitative accuracy. In particular, we study X-ray diffraction and resonant X-ray scattering data recorded on crystalline materials. We demonstrate that weak signals stemming from charge ordering, insignificant in the noisy data, become visible and accurate in the denoised data.