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Hakdong Kim¹, Taeheul Jun², Hyoung Lee³

¹Department of Digital Contents, Sejong University, Seoul, Korea.

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This study introduces a deep learning method to create 3D birefringence images from holographic interference patterns. This non-invasive technique accurately visualizes internal material structures, aiding various industries.

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Area of Science:

Optics and Photonics
Materials Science
Artificial Intelligence

Background:

Refractive index is a key material property for non-invasive 3D interior exploration.
Birefringence, caused by differing refractive indices, splits light polarization and is observed in materials like liquid crystals (LCs).
Visualizing the 3D internal structure of birefringent materials is crucial for advancements in semiconductors, displays, optics, and biomedical fields.

Purpose of the Study:

To develop a novel deep learning approach for generating 3D birefringence images.
To utilize multi-viewed holographic interference images as input for the deep learning model.
To provide a non-invasive method for visualizing the 3D refractive index distribution in materials.

Main Methods:

Acquisition of multi-viewed holographic interference pattern images and 3D birefringence volume images using a polarizing dielectric tensor tomography (DTT)-based microscope system.
Training a deep learning model to generate 3D birefringence volume images from 2D interference pattern image sets.
Performance evaluation against ground truth 3D images obtained directly from DTT microscopy.

Main Results:

The proposed deep learning model successfully generated 3D birefringence images from multi-viewed holographic interference patterns.
Visualization techniques confirmed the accurate representation of refractive index distribution in the generated 3D images.
The method demonstrated efficiency in reconstructing 3D refractive index distributions.

Conclusions:

The developed deep learning approach offers an effective, data-driven alternative to traditional DTT methods for 3D birefringence imaging.
This novel technique enables non-invasive visualization of internal material structures, with potential applications in diverse scientific and industrial sectors.
The study highlights the power of deep learning in advancing optical microscopy and material characterization.