Deep Learning Enabled Scalable Calibration of a Dynamically Deformed Multimode Fiber
Deep Learning Enabled Scalable Calibration of a Dynamically Deformed Multimode Fiber
复制标题
深度学习实现动态变形多模光纤的可扩展校准
DOI：
10.1002/adpr.202100304
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Fan P
中科院分区：
文献类型：
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作者：
Fan P
多模光纤（MMF）是小型化、灵活、大容量的信息通道，有望在内窥镜成像领域开辟新的应用。然而，通过连续变形的MMF进行精确的光控制仍然是一个挑战。这里，提出了一种使用深度学习的动态变形 MMF 的可扩展校准框架。概念验证实验表明，所提出的连续生成对抗模型能够顺序表征 MMF 传输状态，并使用近端反射实时同步检测光纤变形，从而允许通过半柔性 MMF 进行自适应跨状态聚焦，而无需在可扩展校准后进行远端访问。该框架是在极端内存限制下的持续学习方案，其中模型能够合成训练数据并防止忘记先前学习的弯曲状态。所提出的方法为动态变形MMF的可扩展校准的实验实现铺平了道路。
Multimode fibers (MMF) are miniaturized, flexible, and high‐capacity information channels, promising to open up new applications in endoscopic imaging. However, precise light control through an MMF with continuous deformations is still a challenge. Here, a scalable calibration framework for a dynamically deformed MMF using deep learning is proposed. The proof‐of‐concept experiments demonstrate that the proposed continual generative adversarial model has the ability to characterize the MMF transmission states sequentially and detect the fiber deformation using proximal reflection in real‐time synchronously, allowing self‐adaptively cross‐state focusing through a semi‐flexible MMF without distal access after the scalable calibration. This framework is a continual learning scheme under extreme memory constraints where the model is able to synthesize training data and prevent forgetting the previously learned bending states. The proposed method paves the way for the experimental realization of scalable calibration of a dynamically deformed MMF.