  Like other experimental techniques, X-ray Photon Correlation Spectroscopy is
a 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 ED models a
general tool for random noise removal, but their application to low
signal-to-noise data can enhance the data quantitative usage since they are
able to learn the functional form of the signal. We demonstrate that the CNN-ED
models trained on real-world experimental data help to effectively extract
equilibrium dynamics parameters from two-time correlation functions, containing
statistical noise and dynamic heterogeneities. Strategies for optimizing the
models performance and their applicability limits are discussed.
