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Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

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Deep learning-based multimode fiber imaging of multiple objects with different spatial coherence and different

Haosen Pu, Hao Wu, Guowen Zhang

|August 13, 2025

Summary

This summary is machine-generated.

Researchers developed a deep learning model, TDUNet (two-decoder-UNet), to reconstruct images of two objects from a single speckle pattern after passing through a multimode fiber (MMF). The model achieves high-fidelity reconstruction even with low laser spatial coherence.

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Area of Science:

Optics and Photonics
Machine Learning
Image Reconstruction

Background:

Illuminating a single object with a coherent laser through a multimode fiber (MMF) results in a lost object information speckle pattern.
Deep learning methods have shown success in high-fidelity image reconstruction from speckle patterns.
Reconstructing images from complex optical systems, like MMFs, remains a challenge.

Purpose of the Study:

To develop a deep learning model capable of reconstructing images of two objects from a single speckle pattern.
To investigate the model's performance under varying spatial coherence and polarization conditions of the illuminating laser.
To assess the feasibility of image reconstruction in a more general scenario involving multiple objects and planes.

Main Methods:

A novel neural network, TDUNet (two-decoder-UNet), was designed and trained for image reconstruction.
The study involved illuminating two objects in different planes with lasers of varying spatial coherence and orthogonal polarization.
The light passed through a multimode fiber, generating a speckle pattern at the distal end, which was then used for reconstruction.

Main Results:

The trained TDUNet successfully reconstructed high-quality images from single speckle patterns.
The model demonstrated high-fidelity reconstruction of two objects even when the illuminating laser had low spatial coherence.
Experimental results indicated slightly better reconstruction performance for the object in the first plane compared to the second.

Conclusions:

TDUNet is effective for reconstructing images of multiple objects from complex speckle patterns generated by multimode fibers.
The developed deep learning approach overcomes limitations of low spatial coherence in laser illumination for image reconstruction.
This work advances the field of optical image reconstruction using artificial intelligence, particularly for challenging scenarios.