Self-attention-based non-linear basis transformations for compact latent space modelling of dynamic optical fibre transmission matrices
arXiv:2406.07775v2 Announce Type: replace Abstract: Multimode optical fibres are hair-thin strands of glass that efficiently transport light. They promise next-generation medical endoscopes that provide unprecedented sub-cellular image resolution deep inside the body. However, confining light to such fibres means that images are inherently scrambled in transit. Conventionally, this scrambling has been compensated by pre-calibrating how a specific fibre scrambles light and solving a stationary linear matrix equation that represents a physical model of the fibre. However, as the technology devel
The continuous advancements in AI and machine learning techniques, particularly in self-attention mechanisms, are enabling novel solutions to long-standing engineering challenges like optical fibre imaging.
This research could significantly improve the resolution and practical application of sub-cellular endoscopic imaging, expanding diagnostic capabilities and minimally invasive medical procedures.
The conventional linear compensation for optical fibre light scrambling is being challenged by AI-driven non-linear approaches, potentially leading to more accurate and real-time medical imaging.
- · Medical technology sector
- · Diagnostic imaging companies
- · Patients requiring endoscopic procedures
- · AI/ML in medical applications
- · Manufacturers of conventional, less advanced endoscopes
- · Legacy medical imaging techniques in certain applications
Improved internal body imaging capabilities could lead to earlier disease detection and more precise surgical interventions.
The reduced invasiveness and enhanced detail might expand the global market for endoscopic procedures and AI-driven medical devices.
This could set a precedent for AI-driven solutions to other fundamental physics-based challenges in medical diagnostics and beyond, accelerating the development of 'smart' instruments.
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