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arXiv 2004.04603physics.opticscs.LGeess.IV
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具有反射光和深度神经网络的自适应光学

Adaptive optics with reflected light and deep neural networks

Ivan Vishniakou, Johannes D. Seelig

摘要:

光的散射和像差限制了生物组织的光学显微术,这促使了自适应光学技术的发展。在这里,我们开发了一种用于自适应光学的方法,该方法具有与epi检测配置兼容的反射光和深度神经网络。产生由激发和检测路径像差以及相应的反射聚焦图像组成的样品像差的大数据集。这些数据集用于训练深度神经网络。经过训练后,这些网络可以基于从散射样品记录的反射光图像来解开并独立地校正激发和检测像差。类似的深度学习方法也可以通过散射引导星来演示。使用双光子成像验证了预测的像差校正。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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