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Dual-plane Wavefront Sensing Using A Vision Transformer.

Home
Dual-plane Wavefront Sensing Using A Vision Transformer.

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Dual-plane wavefront sensing using a vision transformer.

Evan O'Rourke, Kevin O'Keeffe

|March 18, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Deep learning for wavefront sensing shows promise. A vision transformer model outperforms convolutional neural networks in estimating Zernike coefficients from downsampled images.

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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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Area of Science:

Optics and Photonics
Machine Learning
Image Processing

Background:

Deep learning enables direct estimation of Zernike coefficients from intensity measurements in wavefront sensing.
Convolutional Neural Networks (CNNs) are the predominant deep learning models used for this task.
Limitations exist in CNN performance, particularly with downsampled image data.

Purpose of the Study:

To introduce and evaluate a dual-plane wavefront sensor utilizing a vision transformer (ViT) model.
To compare the performance of the ViT-based sensor against a CNN-based approach.
To assess the efficacy of ViT in handling downsampled image data for Zernike coefficient estimation.

Main Methods:

Development of a dual-plane wavefront sensor architecture.
Training a vision transformer model for wavefront sensing.
Comparative analysis of ViT and CNN performance using experimental and simulation data.
Evaluation of prediction accuracy for high-order Zernike coefficients.

Main Results:

The vision transformer model demonstrated superior performance compared to the CNN.
Outperformance was particularly evident when dealing with significantly downsampled image data.
The ViT model showed enhanced accuracy in predicting high-order Zernike coefficients.

Conclusions:

Vision transformers offer a powerful alternative to CNNs for image-based wavefront sensing.
ViT-based wavefront sensing is particularly advantageous for applications with limited image resolution.
This approach advances the field of optical metrology and adaptive optics.