A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes. Here we propose a fiber imaging approach employing compressive sensing with a data-driven machine learning framework. We implement a generative adversarial network for image reconstruction without relying on a sample sparsity constraint. The proposed method outperforms the conventional compressive imaging algorithms in terms of image quality and noise robustness. We experimentally demonstrate speckle-based imaging below the diffraction limit at a sub-Nyquist speed through a multimode fiber.
@article{arxiv.2201.00601,
title = {Generative adversarial network for super-resolution imaging through a fiber},
author = {Wei Li and Ksenia Abrashitova and Gerwin Osnabrugge and Lyubov V. Amitonova},
journal= {arXiv preprint arXiv:2201.00601},
year = {2022}
}