Neural networks recover fiber images despite bending distortions
Proximal-Only Transmission Matrix Recovery of an Arbitrarily Deformed Graded-Index Multimode Fiber
Computer Vision and Pattern Recognition
Summary
Multimode optical fibers can carry many light signals through a very thin strand, like a tiny cable. However, when the fiber bends or deforms, it changes how light passes through, making it hard to use for imaging inside the body. The authors show that neural networks can figure out how the fiber is bending just by looking at signals from one end. This method helps recover clear images even when the fiber is twisted or curved in complex ways.
What this means in practice
- •For medical device engineers: Design endoscopes that maintain image quality despite fiber bending using neural network recovery techniques.$Commercial implications: Enables advanced fiber endoscopes with stable imaging for minimally invasive medical diagnostics and surgeries.
- •For industrial inspection teams: Improve fiber optic imaging in confined or curved spaces by correcting distortions caused by fiber deformation.
Authors
Cole Reynolds
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 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.