论文标题
深度学习的3D显微镜的实用无传感器像差估计
Practical sensorless aberration estimation for 3D microscopy with deep learning
论文作者
论文摘要
从体积强度图像中估算光学畸变是3D显微镜的无传感器自适应光学器件的关键步骤。基于深度学习的最新方法在快速处理速度下的准确结果。但是，收集训练网络的地面真理显微镜数据通常非常困难甚至不可能，因此在实践中限制了这种方法。在这里，我们证明了仅在模拟数据上训练的神经网络对实际实验图像产生准确的预测。我们验证了以两种不同的显微镜模式获取的模拟和实验数据集的方法，还将结果与非学习方法进行了比较。此外，我们研究了单个畸变在其数据要求方面的可预测性，并发现波前的对称性起着至关重要的作用。最后，我们在Python中免费提供作为开源软件的实施。
Estimation of optical aberrations from volumetric intensity images is a key step in sensorless adaptive optics for 3D microscopy. Recent approaches based on deep learning promise accurate results at fast processing speeds. However, collecting ground truth microscopy data for training the network is typically very difficult or even impossible thereby limiting this approach in practice. Here, we demonstrate that neural networks trained only on simulated data yield accurate predictions for real experimental images. We validate our approach on simulated and experimental datasets acquired with two different microscopy modalities, and also compare the results to non-learned methods. Additionally, we study the predictability of individual aberrations with respect to their data requirements and find that the symmetry of the wavefront plays a crucial role. Finally, we make our implementation freely available as open source software in Python.
