Image transmission through a flexible multimode fiber by deep learning

Abstract

When multimode optical fibers are perturbed, the data that is transmitted through them is scrambled. This presents a major difficulty for many possible applications, such as multimode fiber-based telecommunication and endoscopy. To overcome this challenge, a deep learning approach that generalizes over mechanical perturbations is presented. Using this approach, successful reconstruction of the input images from intensity-only measurements of speckle patterns at the output of a 1.5 meter-long randomly perturbed multimode fiber is demonstrated. The model's success is explained by hidden correlations in the speckle of random fiber conformations.

References in corpus (2)

• Transmission of natural scene images through a multimode fibre
• Compressively sampling the optical transmission matrix of a multimode fibre

Cited by in corpus (6)

• Controlling light propagation in multimode fibers for imaging, spectroscopy and beyond
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• Speckle-correlation imaging through a kaleidoscopic multimode fiber
• Tutorial: How to build and control an all-fiber wavefront modulator using mechanical perturbations
• Model-free estimation of the Cramér-Rao bound for deep-learning microscopy in complex media
• Machine learning-driven complex models for wavefront shaping through multimode fibers