Physics-Informed Graph Neural Network with Frequency-Aware Learning for Optical Aberration Correction

• URL: http://arxiv.org/abs/2512.05683v1
• Date: Fri, 05 Dec 2025 12:58:55 GMT
• Title: Physics-Informed Graph Neural Network with Frequency-Aware Learning for Optical Aberration Correction
• Authors: Yong En Kok, Bowen Deng, Alexander Bentley, Andrew J. Parkes, Michael G. Somekh, Amanda J. Wright, Michael P. Pound,
• Abstract summary: Optical aberrations significantly degrade image quality in microscopy, particularly when imaging deeper into samples.<n>We propose ZRNet, a physics-informed framework that jointly performs Zernike prediction and optical image Restoration.<n>Our approach achieves state-of-the-art performance in both image restoration and Zernike coefficient prediction across diverse microscopy modalities and biological samples with complex, large-amplitudes.
• Score: 36.496034762040054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optical aberrations significantly degrade image quality in microscopy, particularly when imaging deeper into samples. These aberrations arise from distortions in the optical wavefront and can be mathematically represented using Zernike polynomials. Existing methods often address only mild aberrations on limited sample types and modalities, typically treating the problem as a black-box mapping without leveraging the underlying optical physics of wavefront distortions. We propose ZRNet, a physics-informed framework that jointly performs Zernike coefficient prediction and optical image Restoration. We contribute a Zernike Graph module that explicitly models physical relationships between Zernike polynomials based on their azimuthal degrees-ensuring that learned corrections align with fundamental optical principles. To further enforce physical consistency between image restoration and Zernike prediction, we introduce a Frequency-Aware Alignment (FAA) loss, which better aligns Zernike coefficient prediction and image features in the Fourier domain. Extensive experiments on CytoImageNet demonstrates that our approach achieves state-of-the-art performance in both image restoration and Zernike coefficient prediction across diverse microscopy modalities and biological samples with complex, large-amplitude aberrations. Code is available at https://github.com/janetkok/ZRNet.

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