Article Dans Une Revue Proceedings of the National Academy of Sciences of the United States of America Année : 2023

Speckle-correlation imaging through a kaleidoscopic multimode fiber

Résumé

Speckle-correlation imaging techniques are widely used for noninvasive imaging through complex scattering media. While light propagation through multimode fibers and scattering media share many analogies, reconstructing images through multimode fibers from speckle correlations remains an unsolved challenge. Here, we exploit a kaleidoscopic memory effect emerging in square-core multimode fibers and demonstrate fluorescence imaging with no prior knowledge on the fiber. Experimentally, our approach simply requires to translate random speckle patterns at the input of a square-core fiber and to measure the resulting fluorescence intensity with a bucket detector. The image of the fluorescent object is then reconstructed from the autocorrelation of the measured signal by solving an inverse problem. This strategy does not require the knowledge of the fragile deterministic relation between input and output fields, which makes it promising for the development of flexible minimally invasive endoscopes.

Fichier principal
Vignette du fichier
bouchet-et-al-2023-speckle-correlation-imaging-through-a-kaleidoscopic-multimode-fiber.pdf (2.49 Mo) Télécharger le fichier
Origine Publication financée par une institution
Licence
Recherche Data Gouv

Cite 10.57745/B6PSX0 Jeu de données Bouchet, Dorian; Caravaca-Aguirre, Antonio Miguel; Godefroy, Guillaume; Moreau, Philippe; Wang, Irène; Bossy, Emmanuel, 2023, "Speckle-correlation imaging through a kaleidoscopic multimode fiber (experimental data and Python scripts)", https://doi.org/10.57745/B6PSX0, Recherche Data Gouv, V2

Dates et versions

hal-04315337 , version 1 (05-03-2024)

Licence

Identifiants

Citer

Dorian Bouchet, Antonio Miguel Caravaca-Aguirre, Guillaume Godefroy, Philippe Moreau, Irène Wang, et al.. Speckle-correlation imaging through a kaleidoscopic multimode fiber. Proceedings of the National Academy of Sciences of the United States of America, 2023, 120 (26), pp.6027. ⟨10.1073/pnas.2221407120⟩. ⟨hal-04315337⟩
98 Consultations
122 Téléchargements

Altmetric

Partager

  • More