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Related Experiment Video

Updated: Sep 23, 2025

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Real-time complex light field generation through a multi-core fiber with deep learning.

Jiawei Sun^1,2, Jiachen Wu^3,4, Nektarios Koukourakis^5,6

¹Laboratory of Measurement and Sensor System Technique (MST), TU Dresden, Helmholtzstrasse 18, 01069, Dresden, Germany. jiawei.sun@tu-dresden.de.

Scientific Reports

|May 13, 2022

Summary

This summary is machine-generated.

We developed CoreNet, a deep neural network, to rapidly generate tailored computer-generated holograms (CGHs) for multi-core fiber (MCF) microendoscopes. This enables real-time complex light field generation for advanced biomedical applications.

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Last Updated: Sep 23, 2025

Determining 3D Flow Fields via Multi-camera Light Field Imaging

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Published on: March 6, 2013

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Area of Science:

Biomedical optics
Computational imaging
Fiber optics

Background:

Multi-core fiber (MCF) microendoscopes generate complex light fields for biomedicine.
Current methods for generating computer-generated holograms (CGHs) are computationally intensive and slow.
Spatial aliasing from MCFs complicates CGH generation.

Purpose of the Study:

To develop a rapid and accurate method for generating tailored CGHs for MCFs.
To overcome the limitations of iterative algorithms for CGH generation in MCF applications.
To enable advanced applications like real-time fiber-optic cell manipulation.

Main Methods:

A novel deep neural network, CoreNet, was developed using unsupervised learning.
CoreNet generates tailored CGHs for MCFs at near video rates.
Real-time CGHs are loaded onto a spatial light modulator (SLM) for complex light field generation.

Main Results:

CoreNet achieves computation speeds two orders of magnitude faster than previous algorithms.
High-fidelity complex light fields are generated through MCF microendoscopes.
The system operates at near video rates, enabling real-time control.

Conclusions:

CoreNet provides a significant advancement in CGH generation for MCFs.
This technology enables real-time applications such as cell rotation.
It opens new possibilities for high-fidelity light delivery in biomedicine.