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Neural network enabled wide field-of-view imaging with hyperbolic metalenses.

Joel Yeo^1,2,3, Deepak K Sharma¹, Saurabh Srivastava¹

¹Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore.

Nanophotonics (Berlin, Germany)

|November 7, 2025

Summary

Hyperbolic metalenses offer high efficiency but suffer from off-axis aberrations. A Restormer neural network corrects these aberrations, enabling wide field-of-view (FOV) imaging with hyperbolic metalenses.

Keywords:

deconvolution flat optics imaging metalenses neural network

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Area of Science:

Optics and Photonics
Computational Imaging
Artificial Intelligence in Optics

Background:

Metalenses offer ultrathin form factors for advanced sensing and imaging.
Hyperbolic metalenses are known for aberration-free focusing and high efficiency.
Off-axis aberrations in hyperbolic metalenses limit their field-of-view (FOV).

Purpose of the Study:

To correct off-axis aberrations in hyperbolic metalenses.
To enable wide field-of-view (FOV) imaging using hyperbolic metalenses.
To demonstrate a reference-free training method for aberration correction.

Main Methods:

Utilized a Restormer neural network for aberration correction.
Employed the eigen-point-spread function (eigenPSF) method for generating simulated training data.
Trained the neural network using only simulated datasets, avoiding experimental data collection.

Main Results:

Successfully corrected severe off-axis aberrations in hyperbolic metalenses.
Enabled wide FOV imaging (54°) with a hyperbolic metalens camera.
Achieved high-quality imaging reconstructions faithful to the original scene.

Conclusions:

Restormer neural networks can effectively correct hyperbolic metalens aberrations.
Reference-free training on simulated data is a viable approach for optical aberration correction.
Hyperbolic metalens cameras, enhanced by AI, can achieve high-quality, wide-FOV imaging.