Deep Learning Enabled Scalable Calibration of a Dynamically Deformed Multimode Fiber
Abstract
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.
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