Compressively sampling the optical transmission matrix of a multimode fibre
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Abstract (arXiv)
Measurement of the optical transmission matrix (TM) of an opaque material is an advanced form of space-variant aberration correction. Beyond imaging, TM-based methods are emerging in a range of fields including optical communications, optical micro-manipulation, and optical computing. In many cases the TM is very sensitive to perturbations in the configuration of the scattering medium it represents. Therefore applications often require an up-to-the-minute characterisation of the fragile TM, typically entailing hundreds to thousands of probe measurements. In this work we explore how these measurement requirements can be relaxed using the framework of compressive sensing: incorporation of prior information enables accurate estimation from fewer measurements than the dimensionality of the TM we aim to reconstruct. Examples of such priors include knowledge of a memory effect linking input and output fields, an approximate model of the optical system, or a recent but degraded TM measurement. We demonstrate this concept by reconstructing a full-size TM of a multimode fibre supporting 754 modes at compression ratios down to ~5% with good fidelity. The level of compression achievable is dependent upon the strength of our priors. We show in this case that imaging is still possible using TMs reconstructed at compression ratios down to ~1% (8 probe measurements). This compressive TM sampling strategy is quite general and may be applied to any form of scattering system about which we have some prior knowledge, including diffusers, thin layers of tissue, fibre optics of any known refractive profile, and reflections from opaque walls. These approaches offer a route to measurement of high-dimensional TMs quickly or with access to limited numbers of measurements.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jul 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2022Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibrescited by 0
- 2023Efficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learningcited by 0
- 2024Self-attention-based non-linear basis transformations for compact latent space modelling of dynamic optical fibre transmission matricescited by 0
- 2024Self-attention-based non-linear basis transformations for compact latent space modelling of dynamic optical fibre transmission matrices11 Jun 2024
- 2023Efficient data transport over multimode light-pipes with Megapixel images using differentiable ray tracing and Machine-learning16 Jan 2023
- 2022Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres26 Jul 2022