Particle tracking in living systems requires low light exposure and short exposure times to avoid phototoxicity and photobleaching and to fully capture particle motion with high-speed imaging. Low excitation light comes at the expense of tracking accuracy. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure datasets, qualitatively improving the images. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic datasets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional datasets, whereas artifacts were introduced by the denoisers in 3D datasets. Experimentally, we found that, while both supervised and unsupervised approaches improved tracking results compared to the original noisy images, supervised learning generally outperformed the unsupervised approach. We find that nicer-looking image sequences are not synonymous with more precise tracking results and highlight that deep learning algorithms can produce deceiving artifacts with extremely noisy images. Finally, we address the challenge of selecting parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optimal particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of this approach to critically evaluate artificial intelligence solutions for quantitative microscopy.

生命系统中的粒子跟踪需要低光照和短曝光时间，以避免光毒性和光漂白，并通过高速成像完全捕获粒子运动。低激发光是以牺牲跟踪精度为代价的。基于深度学习的图像恢复方法极大地提高了低曝光数据集的信噪比，从质量上改善了图像。然而，尚不清楚这些方法生成的图像是否会产生准确的定量测量，例如（单）粒子跟踪实验中的扩散参数。在这里，我们使用合成数据集和扩散染色质电影作为生物学示例，评估了两种流行的用于粒子跟踪的深度学习降噪软件包的性能。通过合成数据，有监督和无监督的深度学习在二维数据集中以高精度恢复粒子运动，而伪影是由 3D 数据集中的降噪器引入的。通过实验，我们发现，虽然与原始噪声图像相比，监督和无监督方法都改善了跟踪结果，但监督学习通常优于无监督方法。我们发现更好看的图像序列并不等同于更精确的跟踪结果，并强调深度学习算法可以产生具有极其嘈杂图像的欺骗性伪影。最后，我们通过实现快速探索多维参数空间的节俭贝叶斯优化器来解决选择参数以训练卷积神经网络的挑战，识别产生最佳粒子跟踪精度的网络。我们的研究提供了使用深度学习进行图像恢复的定量结果测量。我们预计这种方法将广泛应用于批判性评估定量显微镜的人工智能解决方案。