Compressive ghost imaging through scattering media with deep learning

Fengqiang Li, Ming Zhao, Zhiming Tian, Florian Willomitzer, Oliver Cossairt

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Imaging through scattering media is challenging since the signal to noise ratio (SNR) of the reflection can be heavily reduced by scatterers. Single-pixel detectors (SPD) with high sensitivities offer compelling advantages for sensing such weak signals. In this paper, we focus on the use of ghost imaging to resolve 2D spatial information using just an SPD. We prototype a polarimetric ghost imaging system that suppresses backscattering from volumetric media and leverages deep learning for fast reconstructions. In this work, we implement ghost imaging by projecting Hadamard patterns that are optimized for imaging through scattering media. We demonstrate good quality reconstructions in highly scattering conditions using a 1.6% sampling rate.

Original languageEnglish (US)
Pages (from-to)17395-17408
Number of pages14
JournalOptics Express
Volume28
Issue number12
DOIs
StatePublished - Jun 8 2020

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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