Convolutional neural networks applied to differential dynamic microscopy reduces noise when quantifying heterogeneous dynamics

Quantifying dynamics in non-equilibrium systems is challenged by the competing demands of fine temporal resolution and of sufficient time averaging to reduce noise. We address this by combining differential dynamic microscopy and machine learning.