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Unsupervised speckle denoising in digital holographic interferometry based on 4-f optical simulation integrated

HongBo Yu, Qiang Fang, QingHe Song

|June 10, 2024

Summary

A novel self-supervised deep learning method effectively reduces speckle noise in digital holographic interferometry (DHI). This cycle-consistent adversarial network improves accuracy for both simulated and experimental data.

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Area of Science:

Optics and Photonics
Artificial Intelligence
Image Processing

Background:

Speckle noise in digital holographic interferometry (DHI) is an inherent challenge that compromises measurement accuracy.
Existing denoising methods often struggle with the complexity and variability of speckle noise, limiting their effectiveness.

Purpose of the Study:

To develop and evaluate a self-supervised deep learning approach for mitigating speckle noise in DHI.
To enhance the accuracy and reliability of DHI measurements by effectively removing speckle noise.

Main Methods:

A cycle-consistent generative adversarial network (GAN) was employed for speckle denoising.
The method incorporates a 4-f optical speckle noise simulation module and a parameter generator.
Training utilized an unpaired dataset, overcoming the limitations of acquiring noise-free and paired experimental data.

Main Results:

The proposed deep learning method demonstrated superior speckle denoising performance on both simulated and experimental DHI data.
Achieved a 6.9% performance improvement over conventional methods and a 2.6% improvement over unsupervised deep learning in peak signal-to-noise ratio (PSNR).

Conclusions:

The self-supervised deep learning method offers a robust and effective solution for speckle noise reduction in DHI.
The approach shows significant potential for improving DHI applications, especially in processing large datasets.