English(EN) Proximal-Only Transmission Matrix Recovery of an Arbitrarily Deformed Graded-Index Multimode Fiber

机器学习实现变形多模光纤传输矩阵恢复

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种新颖的方法，仅使用近端测量来恢复任意变形的渐变折射率多模光纤的传输矩阵。这一进展对于实现通用多模光纤内窥镜检查具有重要意义，因为以前的内窥镜检查受到传输矩阵对光纤变形敏感性的限制。新方法利用机器学习，特别是神经网络，来泛化和准确恢复这些矩阵，克服了任意光纤变形带来的挑战。 AI

影响这项研究通过提高光纤传输光的能力，可能带来更强大、更多功能的内窥镜成像技术。

排序理由该集群包含一篇详细介绍光学领域新技术的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.CV TIER_1 English(EN) · Cole Reynolds · 2026-09-15 04:00

任意变形梯度折射率多模光纤的近端仅传输矩阵恢复

arXiv:2609.14869v1 Announce Type: cross Abstract: 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…