The University of Southampton
University of Southampton Institutional Repository

Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array

Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array
Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array

We demonstrate the use of deep learning for fast spectral deconstruction of speckle patterns. The artificial neural network can be effectively trained using numerically constructed multispectral datasets taken from a measured spectral transmission matrix. Optimized neural networks trained on these datasets achieve reliable reconstruction of both discrete and continuous spectra from a monochromatic camera image. Deep learning is compared to analytical inversion methods as well as to a compressive sensing algorithm and shows favourable characteristics both in the oversampling and in the sparse undersampling (compressive) regimes. The deep learning approach offers significant advantages in robustness to drift or noise and in reconstruction speed. In a proof-of-principle demonstrator we achieve real time recovery of hyperspectral information using a multi-core, multi-mode fiber array as a random scattering medium.

1094-4087
20965-20979
Kürüm, Ulas
bc2d1c4c-5c06-460e-a2c8-0750d8e60f7d
Wiecha, Peter R.
fb335482-9577-41af-a0ef-3988b7654c9b
French, Rebecca
d6d6a85a-e351-4cc8-ae4a-827c35fe6b64
Muskens, Otto L.
2284101a-f9ef-4d79-8951-a6cda5bfc7f9
Kürüm, Ulas
bc2d1c4c-5c06-460e-a2c8-0750d8e60f7d
Wiecha, Peter R.
fb335482-9577-41af-a0ef-3988b7654c9b
French, Rebecca
d6d6a85a-e351-4cc8-ae4a-827c35fe6b64
Muskens, Otto L.
2284101a-f9ef-4d79-8951-a6cda5bfc7f9

Kürüm, Ulas, Wiecha, Peter R., French, Rebecca and Muskens, Otto L. (2019) Deep learning enabled real time speckle recognition and hyperspectral imaging using a multimode fiber array. Optics Express, 27 (15), 20965-20979. (doi:10.1364/OE.27.020965).

Record type: Article

Abstract

We demonstrate the use of deep learning for fast spectral deconstruction of speckle patterns. The artificial neural network can be effectively trained using numerically constructed multispectral datasets taken from a measured spectral transmission matrix. Optimized neural networks trained on these datasets achieve reliable reconstruction of both discrete and continuous spectra from a monochromatic camera image. Deep learning is compared to analytical inversion methods as well as to a compressive sensing algorithm and shows favourable characteristics both in the oversampling and in the sparse undersampling (compressive) regimes. The deep learning approach offers significant advantages in robustness to drift or noise and in reconstruction speed. In a proof-of-principle demonstrator we achieve real time recovery of hyperspectral information using a multi-core, multi-mode fiber array as a random scattering medium.

Text
oe-27-15-20965 - Version of Record
Available under License Creative Commons Attribution.
Download (9MB)

More information

e-pub ahead of print date: 12 June 2019

Identifiers

Local EPrints ID: 433574
URI: http://eprints.soton.ac.uk/id/eprint/433574
ISSN: 1094-4087
PURE UUID: bb70cb4e-6185-494c-859b-cae606037318
ORCID for Otto L. Muskens: ORCID iD orcid.org/0000-0003-0693-5504

Catalogue record

Date deposited: 28 Aug 2019 16:30
Last modified: 10 Sep 2019 00:40

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×