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Related Experiment Video

Updated: May 8, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

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Seeing through multimode fibers using real-valued intensity transmission matrix with deep learning.

Ziyu Ye, Tianrui Zhao, Wenfeng Xia

|April 12, 2025

Summary

This study introduces a hybrid approach using a real-valued intensity transmission matrix (RVITM) and deep learning to enhance image retrieval through multimode fibers (MMFs). The method improves image quality and generalization for applications in endoscopy and telecommunications.

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Area of Science:

Optical physics
Image processing
Machine learning

Background:

Multimode fibers (MMFs) enable image and data transmission but suffer from distortions due to mode dispersion and coupling.
Existing deep learning methods for MMF image transmission require extensive data and have limited generalization.
Distortions in MMFs hinder applications in biomedical endoscopy and telecommunications.

Purpose of the Study:

To develop a hybrid approach combining a real-valued intensity transmission matrix (RVITM) and deep learning for improved image retrieval through MMFs.
To enhance image quality and generalization capabilities for MMF-based applications.
To reduce the training data requirements and convergence time compared to purely deep learning methods.

Main Methods:

Characterizing the MMF and retrieving initial images using a RVITM algorithm.
Refining image quality with a hierarchical, parallel multi-scale (HPM)-attention U-Net.
Evaluating performance using metrics like SSIM and PSNR.

Main Results:

Achieved high-quality image reconstructions with SSIM up to 0.9524 and PSNR up to 33.244 dB.
Demonstrated strong generalization capabilities, requiring fewer training samples.
Showed faster convergence compared to purely deep learning-based methods.

Conclusions:

The proposed hybrid RVITM and deep learning approach effectively enhances image retrieval through MMFs.
This method offers a promising solution for ultrathin endoscopy and spatial-mode multiplexing in telecommunications.
The approach overcomes limitations of existing methods by improving efficiency and generalization.