High-quality ghost imaging through highly complex scattering media with physics-enhanced untrained neural networks

Tianshun Zhang , Yang Peng , Wen Chen

Optics Express +

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September 23, 2025

View abstract on PubMed

Summary

This study introduces a physics-enhanced untrained neural network (UNN) to improve ghost imaging (GI) through scattering media. The method reconstructs objects effectively even when both light paths are disturbed, overcoming complex optical challenges.

Area Of Science

Optics and Photonics
Computational Imaging
Machine Learning Applications

Background

Optical imaging through scattering media is hindered by distorted illumination and detection paths.
Ghost imaging (GI) performance degrades due to dynamic scaling factors in complex scattering environments.

Purpose Of The Study

To develop a novel method for high-quality object reconstruction in optical imaging through complex scattering media.
To overcome limitations of ghost imaging caused by simultaneous disturbances in illumination and detection paths.

Main Methods

Utilized a physics-enhanced untrained neural network (UNN) integrated with a ghost imaging (GI) model.
Employed rotating ground glass diffusers and a turbidity-varying liquid chamber to create complex scattering.
Recorded speckle patterns and single-pixel intensities, with UNN estimating dynamic scaling factors.

Main Results

Achieved robust and high-quality object reconstruction despite complex and dynamic scattering.
Demonstrated effective compensation for beam distortions in both illumination and detection paths.
Validated the capability of the physics-enhanced UNN to correct measurements for reliable reconstruction.

Conclusions

The proposed physics-enhanced UNN method enables robust optical imaging through simultaneously disturbed scattering media.
This approach offers a promising solution for overcoming optical scattering challenges in diverse complex scenarios.
Opens new avenues for advanced imaging techniques in turbid and dynamic environments.