Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres

Mitton, J., Mekhail, S. , Padgett, M. , Faccio, D. , Aversa, M. and Murray-Smith, R. (2022) Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres. In: 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, Louisiana, United States of America, 28th November - 9th December 2022,

Abstract

We develop a new type of model for solving the task of inverting the transmission effects of multi-mode optical fibres through the construction of an SO+(2, 1) equivariant neural network. This model takes advantage of the of the azimuthal correlations known to exist in fibre speckle patterns and naturally accounts for the difference in spatial arrangement between input and speckle patterns. In addition, we use a second post-processing network to remove circular artifacts, fill gaps, and sharpen the images, which is required due to the nature of optical fibre transmission. This two stage approach allows for the inspection of the predicted images produced by the more robust physically motivated equivariant model, which could be useful in a safety-critical application, or by the output of both models, which produces high quality images. Further, this model can scale to previously unachievable resolutions of imaging with multi-mode optical fibres and is demonstrated on 256 × 256 pixel images. This is a result of improving the trainable parameter requirement from O(N 4) to O(m), where N is pixel size and m is number of fibre modes. Finally, this model generalises to new images, outside of the set of training data classes, better than previous models.

• Item Type: Conference Proceedings
• Additional Information: JM is supported by a University of Glasgow Lord Kelvin Adam Smith Studentship. RM-S, MP, DF and SPM are grateful for EPSRC support through grants EP/T00097X/1 and RM-S for EP/R018634/1. MA is funded by dotPhoton and a UofG scholarship. DF acknowledges funding through the Royal Academy of Engineering Chair in Emerging Technologies programme.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Murray-Smith, Professor Roderick and Mitton, Joshua and Faccio, Professor Daniele and Mekhail, Mr Simon and Aversa, Mr Marco and Padgett, Professor Miles
• Authors: Mitton, J., Mekhail, S., Padgett, M., Faccio, D., Aversa, M., and Murray-Smith, R.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Computing Science; College of Science and Engineering > School of Physics and Astronomy
• Copyright Holders: Copyright © 2022 The Authors
• Publisher Policy: Reproduced with the permission of the author

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• Organisation

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
305567		QuantIC - The UK Quantum Technoogy Hub in Quantum Enhanced Imaging	Miles Padgett	Engineering and Physical Sciences Research Council (EPSRC)	EP/T00097X/1	P&S - Physics & Astronomy
300982		Exploiting Closed-Loop Aspects in Computationally and Data Intensive Analytics	Roderick Murray-Smith	Engineering and Physical Sciences Research Council (EPSRC)	EP/R018634/1	Computing Science