Looking and Listening in Complex Media

Lead Research Organisation: University of Glasgow
Department Name: School of Physics and Astronomy

Abstract

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Publications

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Starshynov I (2019) Coherent Control of Light for Non-Line-of-Sight Imaging in Physical Review Applied

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Johnson S (2023) Hiding images in noise. in Optics express

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O'Donnell L (2021) Optimising backscatter from multiple beam interference in Optics Express

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Johnson S (2022) Single-pixel imaging with heralded single photons in Optics Continuum

 
Description We have devised a method by which it is possible to scatter light from a wall and then refocus it to a spot that can then be scanned across a scene. This effectively transforms the wall into a sort of mirror and scanning the spot in reflection from the wall/mirror allows to perform a lidar measurement of a hidden environment or behind a corner. Indeed, we showed this capability to build a 2D image of a hidden object. A similar approach can also be used to detect very faint vibrations (for example a heart beat) from behind a corner.
We have also shown that by using machine learning that it is possible image through multiple layers of scattering media that mimic a thick scattering sample. This is the first time that anyone proves imaging capability through thick scattering media, i.e. in regimes that cannot be assessed using standard techniques based on correlations of the transmitted light.

Moving beyond these findings, we have discovered that it is possible to use machine learning approaches to image through thick diffusive materials in regimes (of strong scattering) that were not previously thought possible. Information theory is shedding light on how this is possible and could have applications for imaging through optical fibres and changing random media such as fog.
This work also has impact on the problem of physical unclonable functions, i.e. unclonable security devices that rely on the unclonability of complex or random media. Wokr is progressing to evaluate the limits within which our results imply that unclonable keys are can actually be hacked.
Exploitation Route We believe that these findings will be useful to other researchers both worldwide but also simply within our research group and within QuantIC, the quantum hub for imaging. Indeed, the same concepts could be used for medical imaging and imaging inside the body.
Sectors Healthcare,Security and Diplomacy

 
Title Hiding images in noise 
Description The analysis code and data for the paper 'Hiding images in noise'. The data comprises of 1 second acquisitions from a gated intensified camera for both the correlated and uncorrelated measurements. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL http://researchdata.gla.ac.uk/id/eprint/1362
 
Title Remote laser-speckle sensing of heart sounds for health assessment and biometric identification 
Description The paper describes a contactless, machine-learning assisted method for heart-sound identification and quantification based on the remote measurement of the reflected laser speckle from the human neck skin surface. For more details see Readme.rtf 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL http://researchdata.gla.ac.uk/id/eprint/1238
 
Title Single-pixel imaging with heralded single photons 
Description  
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
URL http://researchdata.gla.ac.uk/id/eprint/1215
 
Title Statistical Dependencies Beyond Linear Correlations in Light Scattered by Disordered Media 
Description The purpose of this dataset is to demonstrate the existence of a nonlinear correlation in randomly scattered fields that connects different realisations of the scattering medium and to show an example of image reconstruction through a scattering medium in the absence of any known linear correlations in particular speckle memory effect. For more details see Readme.pdf 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL http://researchdata.gla.ac.uk/id/eprint/1089