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Poster
Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres
Oisín Moran · Piergiorgio Caramazza · Daniele Faccio · Roderick Murray-Smith

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #144

We use complex-weighted, deep networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale input images generated with coherent light, by measuring only image amplitude (not amplitude and phase as is typical) at the output of \SI{1}{\metre} and \SI{10}{\metre} long, \SI{105}{\micro\metre} diameter multimode fibre. This data is made available as the {\it Optical fibre inverse problem} Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches. A {\it unitary regularisation} approach for complex-weighted networks is proposed which performs well in robustly inverting the fibre transmission matrix, which fits well with the physical theory. A key benefit of the unitary constraint is that it allows us to learn a forward unitary model and analytically invert it to solve the inverse problem. We demonstrate this approach, and show how it can improve performance by incorporating knowledge of the phase shift induced by the spatial light modulator.

Author Information

Oisín Moran (Inscribe.ai)
Piergiorgio Caramazza (University of Glasgow)
Daniele Faccio (University of Glasgow)
Rod Murray-Smith (University of Glasgow)

Roderick Murray-Smith is a Professor of Computing Science at Glasgow University, leading the Inference, Dynamics and Interaction research group, and heads the 50-strong Section on Information, Data and Analysis, which also includes the Information Retrieval, Computer Vision & Autonomous systems and IDEAS Big Data groups. He works in the overlap between machine learning, interaction design and control theory. In recent years his research has included multimodal sensor-based interaction with mobile devices, mobile spatial interaction, AR/VR, Brain-Computer interaction and nonparametric machine learning. Prior to this he held positions at the Hamilton Institute, NUIM, Technical University of Denmark, M.I.T. (Mike Jordan’s lab), and Daimler-Benz Research, Berlin, and was the Director of SICSA, the Scottish Informatics and Computing Science Alliance (all academic CS departments in Scotland). He works closely with the mobile phone industry, having worked together with Nokia, Samsung, FT/Orange, Microsoft and Bang & Olufsen. He was a member of Nokia's Scientific Advisory Board and a member of the Scientific Advisory Board for the Finnish Centre of Excellence in Computational Inference Research. He has co-authored three edited volumes, 29 journal papers, 18 book chapters, and 88 conference papers. http://www.dcs.gla.ac.uk/~rod/ http://www.dcs.gla.ac.uk/~rod/Publications.htm

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