Opt Express. 2023 Jun 5;31(12):20049-20067. doi: 10.1364/OE.486741.
Holographic cloud probes provide unprecedented information on cloud particle density, size and position. Each laser shot captures particles within a large volume, where images can be computationally refocused to determine particle size and location. However, processing these holograms with standard methods or machine learning (ML) models requires considerable computational resources, time and occasional human intervention. ML models are trained on simulated holograms obtained from the physical model of the probe since real holograms have no absolute truth labels. Using another processing method to produce labels would be subject to errors that the ML model would subsequently inherit. Models perform well on real holograms only when image corruption is performed on the simulated images during training, thereby mimicking non-ideal conditions in the actual probe. Optimizing image corruption requires a cumbersome manual labeling effort. Here we demonstrate the application of the neural style translation approach to the simulated holograms. With a pre-trained convolutional neural network, the simulated holograms are "stylized" to resemble the real ones obtained from the probe, while at the same time preserving the simulated image "content" (e.g. the particle locations and sizes). With an ML model trained to predict particle locations and shapes on the stylized data sets, we observed comparable performance on both simulated and real holograms, obviating the need to perform manual labeling. The described approach is not specific to holograms and could be applied in other domains for capturing noise and imperfections in observational instruments to make simulated data more like real world observations.
全息云探针提供了前所未有的云粒子密度、大小和位置信息。每次激光射击都会捕获大体积内的粒子，在这些粒子中可以通过计算重新聚焦来确定粒子的大小和位置。然而，使用标准方法或机器学习 (ML) 模型处理这些全息图需要大量的计算资源、时间和偶尔的人工干预。ML 模型是在探针的物理模型获得的模拟全息图上进行训练的，因为真实全息图没有绝对的真实标签。使用另一种处理方法生成标签将受到 ML 模型随后继承的错误的影响。只有在训练过程中对模拟图像进行图像损坏时，模型才能在真实全息图上表现良好，从而模拟实际探针中的非理想条件。优化图像损坏需要繁琐的手动标记工作。在这里，我们展示了神经风格转换方法在模拟全息图上的应用。使用预先训练的卷积神经网络，模拟全息图被“风格化”以类似于从探针获得的真实全息图，同时保留模拟图像的“内容”（例如粒子位置和大小）。通过在风格化数据集上训练预测粒子位置和形状的 ML 模型，我们观察到在模拟和真实全息图上都具有可比的性能，从而无需进行手动标记。所描述的方法不仅限于全息图，并且可以应用于其他领域，以捕获观测仪器中的噪声和不完美之处，从而使模拟数据更接近实际观测结果。
