任意变形渐变折射率多模光纤的近端仅透射矩阵恢复

中文摘要 AI 辅助

多模光纤是可用的最细成像导管之一，通过与人发相当的横截面承载数百至数千个空间模式，但其内窥镜能力目前受限于透射矩阵对光纤变形状态的敏感性。仅近端恢复光纤透射矩阵是推动通用多模光纤内窥镜应用的一种有吸引力的方法，在过去十年中，机器学习技术已被应用于单端和双端透射矩阵恢复任务。我们提出了一种解决这一跨学科问题的新方法，并表明神经网络能够泛化，仅从近端测量中恢复任意变形渐变折射率多模光纤的透射矩阵。

英文摘要

The multimode fiber is among the thinnest imaging conduits available, carrying hundreds to thousands of spatial modes through a cross-section comparable to a human hair, but its endoscopic capabilities are currently limited by the sensitivity of the transmission matrix to the fiber's deformed state. Proximal-only recovery of the fiber's transmission matrix is an appealing approach for enabling general use multimode fiber endoscopy, and within the last decade, machine learning techniques have been applied to both single-ended and double-ended transmission matrix recovery tasks. We present a new approach to this interdisciplinary problem and show that neural networks can generalize to recover transmission matrices of an arbitrarily deformed graded-index multimode fiber from proximal measurements alone.

发表机构

• Weyl Labs（外尔实验室）

机构由 AI 辅助整理，请以论文原文为准。