Optical focusing through scattering media is of great significance yet challenging in lots of scenarios, including biomedical imaging, optical communication, cybersecurity, three-dimensional displays, etc. Wavefront shaping is a promising approach to solve this problem, but most implementations thus far have only dealt with static media, which, however, deviates from realistic applications. Herein, we put forward a deep learning-empowered adaptive framework, which is specifically implemented by a proposed Timely-Focusing-Optical-Transformation-Net (TFOTNet), and it effectively tackles the grand challenge of real-time light focusing and refocusing through time-variant media without complicated computation. The introduction of recursive fine-tuning allows timely focusing recovery, and the adaptive adjustment of hyperparameters of TFOTNet on the basis of medium changing speed efficiently handles the spatiotemporal non-stationarity of the medium. Simulation and experimental results demonstrate that the adaptive recursive algorithm with the proposed network significantly improves light focusing and tracking performance over traditional methods, permitting rapid recovery of an optical focus from degradation. It is believed that the proposed deep learning-empowered framework delivers a promising platform towards smart optical focusing implementations requiring dynamic wavefront control.

中文翻译：

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迈向智能光学聚焦：通过非平稳散射介质实现深度学习的动态波前整形

通过散射介质进行光学聚焦具有重要意义，但在许多场景中都具有挑战性，包括生物医学成像、光通信、网络安全、三维显示等。 波前整形是解决这一问题的一种很有前景的方法，但迄今为止大多数实现只有处理静态媒体，然而，这偏离了现实应用。在此，我们提出了一个深度学习赋能的自适应框架，该框架由提出的 Timely-Focusing-Optical-Transformation-Net (TFOTNet) 专门实现，它有效地解决了实时光聚焦和时间重新聚焦的巨大挑战-无需复杂计算的可变媒体。递归微调的引入，让对焦及时恢复，TFOTNet超参数在介质变化速度的基础上自适应调整，有效地处理了介质的时空非平稳性。仿真和实验结果表明，与传统方法相比，具有所提出网络的自适应递归算法显着提高了光聚焦和跟踪性能，允许从退化中快速恢复光学焦点。据信，所提出的深度学习授权框架为需要动态波前控制的智能光学聚焦实现提供了一个有前途的平台。仿真和实验结果表明，与传统方法相比，具有所提出网络的自适应递归算法显着提高了光聚焦和跟踪性能，允许从退化中快速恢复光学焦点。据信，所提出的深度学习授权框架为需要动态波前控制的智能光学聚焦实现提供了一个有前途的平台。仿真和实验结果表明，与传统方法相比，具有所提出网络的自适应递归算法显着提高了光聚焦和跟踪性能，允许从退化中快速恢复光学焦点。据信，所提出的深度学习授权框架为需要动态波前控制的智能光学聚焦实现提供了一个有前途的平台。