Generative Adversarial Network for Superresolution Imaging through a Fiber
Phys. Rev. Appl. , Volume 18 - Issue 3 p. 034075: 1- 9
A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes. However, such a miniaturization usually comes as a cost of a low spatial resolution and a long acquisition time. Here we propose a fast superresolution-fiber-imaging technique employing compressive sensing through a multimode fiber with a data-driven machine-learning framework. We implement a generative adversarial network (GAN) to explore the sparsity inherent to the model and provide compressive reconstruction images that are not sparse in a representation basis. The proposed method outperforms other widespread compressive imaging algorithms in terms of both image quality and noise robustness. We experimentally demonstrate machine-learning ghost imaging below the diffraction limit at a sub-Nyquist speed through a thin multimode fiber probe. We believe that this work has great potential in applications in various fields ranging from biomedical imaging to remote sensing.
|Dutch Ministry of Economic Affairs and Climate Policy, Toeslag voor Topconsortia voor Kennis en Innovatie (TKI)|
|Phys. Rev. Appl.|
|Organisation||Nanoscale Imaging and Metrology|
Li, W, Abrashitova, K, Osnabrugge, G, & Amitonova, L.V. (2022). Generative Adversarial Network for Superresolution Imaging through a Fiber. Phys. Rev. Appl., 18(3), 034075: 1–034075: 9. doi:10.1103/physrevapplied.18.034075