Physics-Informed Graph Neural Network with Frequency-Aware Learning for Optical Aberration Correction
This addresses image quality degradation in microscopy, particularly for deeper imaging, by integrating optical physics into the correction process, representing an incremental improvement over existing methods.
The paper tackles optical aberration correction in microscopy by proposing ZRNet, a physics-informed graph neural network that jointly predicts Zernike coefficients and restores images, achieving state-of-the-art performance across diverse microscopy modalities and samples with complex aberrations.
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.