论文标题
具有反射光和深神经网络的自适应光学器件
Adaptive optics with reflected light and deep neural networks
论文作者
论文摘要
光散射和像差限制了生物组织中的光学显微镜，这激发了自适应光学技术的发展。在这里，我们开发了一种具有反射光和深神经网络与Epi-tection配置兼容的自适应光学器件的方法。由激发和检测路径像差以及相应反射焦点图像组成的样本畸变的大数据集。这些数据集用于培训深神经网络。训练后，这些网络可以根据从散射样品中记录的反射光图像来分离并独立纠正激发和检测像差。散射指南恒星也证明了类似的深度学习方法。使用两个光子成像验证了预测的像差校正。
Light scattering and aberrations limit optical microscopy in biological tissue, which motivates the development of adaptive optics techniques. Here, we develop a method for adaptive optics with reflected light and deep neural networks compatible with an epi-detection configuration. Large datasets of sample aberrations which consist of excitation and detection path aberrations as well as the corresponding reflected focus images are generated. These datasets are used for training deep neural networks. After training, these networks can disentangle and independently correct excitation and detection aberrations based on reflected light images recorded from scattering samples. A similar deep learning approach is also demonstrated with scattering guide stars. The predicted aberration corrections are validated using two photon imaging.
