Weak signal extraction enabled by deep neural network denoising of diffraction data.

ABSTRACT: The removal or cancellation of noise has wide-spread applications in imaging and acoustics. In applications in everyday life, 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 substantially reduces the applicability of simulation-based approaches. Here we show how scientific data can be denoised by using 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 cryst

SUBMITTER: Oppliger J 

PROVIDER: S-EPMC10883886 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature