Reconstruction of partially obscured objects with a physics-driven self-training neural network

Mingjun Xiang , Kai Zhou , Hui Yuan

Optics Express +

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June 14, 2025

View abstract on PubMed

Summary

This study introduces a new deep learning method for 3D terahertz (THz) imaging, reconstructing scenes from power-only data. The approach effectively handles partially obscured objects and minimizes interference, even with limited data.

Area Of Science

Optics and Photonics
Artificial Intelligence
Imaging Science

Background

Terahertz (THz) imaging offers unique capabilities for non-ionizing 3D scene reconstruction.
Traditional holographic imaging requires phase information, which is often unavailable.
Reconstructing 3D scenes from power-only THz data presents significant challenges due to missing phase information.

Purpose Of The Study

To develop an artificial intelligence-supported method for 3D holographic imaging using coherent terahertz radiation.
To reconstruct 3D scenes from intensity-only measurements, overcoming the limitation of phase retrieval.
To address challenges in reconstructing partially obscured objects and minimizing interference in THz imaging.

Main Methods

A physics-informed deep learning (DL) algorithm was developed, incorporating angular spectrum theory as prior knowledge.
A synthetic dataset of diffraction patterns was generated using object information.
A physics-informed neural network (NN) was self-trained using both synthetic and unlabeled experimental THz data.
A recursive training strategy was employed, where the NN iteratively predicted and reincorporated images into the training set.

Main Results

The DL approach successfully reconstructed 3D scenes from power-only THz images.
The method demonstrated effectiveness in reconstructing partially obscured objects.
Mutual interference during object reconstruction was minimized.
The approach showed robustness and effectiveness in data-scarce scenarios.

Conclusions

The proposed physics-informed DL method significantly advances 3D THz imaging capabilities.
This technique provides a viable solution for reconstructing 3D scenes when phase information is absent.
The self-training strategy enables effective learning from limited and unlabeled experimental data, paving the way for practical applications.