Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder-decoder models.

ABSTRACT: Like other experimental techniques, X-ray photon correlation spectroscopy is subject to various kinds of noise. Random and correlated fluctuations and heterogeneities can be present in a two-time correlation function and obscure the information about the intrinsic dynamics of a sample. Simultaneously addressing the disparate origins of noise in the experimental data is challenging. We propose a computational approach for improving the signal-to-noise ratio in two-time correlation functions that is based on convolutional neural network encoder-decoder (CNN-ED) models. Such models extract features from an image via convolutional layers, project them to a low dimensional space and then reconstruct a clean image from this reduced representation via transposed convolutional layers. Not only are

SUBMITTER: Konstantinova T 

PROVIDER: S-EPMC8292438 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature