标题： 卷积神经网络应用于差分动态显微镜在量化异质动力学时可减少噪声 显示英文标题

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

摘要： 差分动态显微镜（DDM）通常依赖包含数百或数千帧的视频来准确量化软物质系统中的运动。 使用持续时间较短的视频会产生更嘈杂和不准确的结果。 这限制了DDM在动力学在长时间内保持平稳的情况下的应用。 在这里，我们研究一种去噪DDM过程的方法，特别适用于可用成像帧数量有限或动力学随时间快速演变的情况。 我们使用卷积神经网络编码器-解码器（CNN-ED）模型来减少通过DDM计算的中间散射函数中的噪声。 我们在含有扩散的微米级胶体颗粒的样品上展示了结合机器学习和DDM的方法。 我们量化了粒子的扩散系数如何随时间变化，当它们悬浮的流体凝胶化时。 我们还量化了在含有粘度梯度的样品中，粒子的扩散系数如何随位置变化。 这些测试案例展示了非平衡动力学研究和高通量筛选如何从一种能够去噪DDM输出的方法中受益。 显示英文摘要

摘要： Differential dynamic microscopy (DDM) typically relies on movies containing hundreds or thousands of frames to accurately quantify motion in soft matter systems. Using movies much shorter in duration produces noisier and less accurate results. This limits the applicability of DDM to situations where the dynamics are stationary over extended times. Here, we investigate a method to denoise the DDM process, particularly suited to when a limited number of imaging frames are available or when dynamics are quickly evolving in time. We use a convolutional neural network encoder-decoder (CNN-ED) model to reduce the noise in the intermediate scattering function that is computed via DDM. We demonstrate this approach of combining machine learning and DDM on samples containing diffusing micron-sized colloidal particles. We quantify how the particles' diffusivities change over time as the fluid they are suspended in gels. We also quantify how the diffusivity of particles varies with position in a sample containing a viscosity gradient. These test cases demonstrate how studies of non-equilibrium dynamics and high-throughput screens could benefit from a method to denoise the outputs of DDM.