Work

Deep learning framework for rapid aberration correction in reflection matrix microscopy

Year: 2025

Type: article

Abstract: Aberrations caused by angle-dependent phase distortions in scattering media degrade image quality in optical imaging. In this study, we present a deep learning-based aberration correction method for reflection matrix microscopy that iteratively corrects both input and output aberrations. By leveraging the structural consistency of output aberrations across different incident wavevectors, we train a U-Net-based model to predict and correct aberrations directly from the reflection matrix. Our iterative inference and correction process effectively eliminates round-trip aberrations, significantly enhancing imaging quality. To further improve efficiency, we introduce a covariance matrix-based training strategy, eliminating the need for explicit input aberration correction and reducing iteration time by half. Our approach achieves a 100-fold computational speedup over conventional wave correlation-based algorithms while maintaining high correction accuracy. We validate our method through numerical simulations and experimental data, demonstrating robustness across various aberration conditions. This deep-learning framework enables real-time, label-free imaging, overcoming computational bottlenecks in aberration correction and paving the way for rapid, high-resolution imaging in biomedical applications where real-time aberration correction is essential.

Source: Optics Express

DOI: 10.1364/oe.563352

Authors: Eunyoung Seong, Dong-Young Kim, Wonshik Choi, Ye-Ryoung Lee

Institution: Korea University

Language: English

Topic: Random lasers and scattering media

Subfield: Acoustics and Ultrasonics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: POSCO TJ Park Foundation, Konkuk University, National Research Foundation of Korea