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Updated: May 28, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Common-path ghost imaging through complex media with dual polarization.

Zian Wang, Tianshun Zhang, Yin Xiao

|February 14, 2025

Summary

This summary is machine-generated.

This study introduces a common-path ghost imaging (GI) setup using dual polarization to overcome challenges posed by scattering media. The novel method achieves high-quality imaging in complex environments without additional algorithms.

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Last Updated: May 28, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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Published on: February 8, 2014

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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Published on: March 20, 2017

Area of Science:

Optics and Photonics
Image Reconstruction

Background:

Ghost imaging (GI) performance is degraded by dynamic and complex scattering media.
Existing GI techniques struggle in challenging free-space environments.

Purpose of the Study:

To design a robust common-path GI (CPGI) setup for complex scattering environments.
To enhance imaging quality and stability in the presence of dynamic scattering.

Main Methods:

A CPGI setup utilizing dual polarization (s-light and p-light) was designed.
Mutually perpendicular polarization states were generated and overlapped in free space.
Dynamic scaling factors from scattering media were corrected using the optical path.

Main Results:

The proposed CPGI method demonstrated high robustness in complex media.
High-quality imaging was achieved despite dynamic scattering.
The method corrected for dynamic scaling factors effectively.

Conclusions:

The dual-polarization CPGI setup offers a simplified and effective solution for imaging in complex scattering environments.
This approach enhances GI performance without requiring additional computational algorithms.
The method facilitates broader applications of GI in challenging conditions.