Generative adversarial network for super-resolution imaging through a fiber

Wei Li; Ksenia Abrashitova; Gerwin Osnabrugge; Lyubov V. Amitonova

doi:10.1103/physrevapplied.18.034075

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arXiv · 2201.00601

Generative adversarial network for super-resolution imaging through a fiber

Wei Li·Ksenia Abrashitova·Gerwin Osnabrugge·Lyubov V. Amitonova

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Abstract

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.

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Wei Li, Ksenia Abrashitova, Gerwin Osnabrugge, Lyubov V. Amitonova. 2022-01-03. Generative adversarial network for super-resolution imaging through a fiber. https://doi.org/10.1103/physrevapplied.18.034075

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