Calibration-free quantitative phase imaging using data-driven aberration modeling
Image and Video Processing
2020-12-02 v1 Optics
Abstract
We present a data-driven approach to compensate for optical aberration in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measurements or a background region to correct aberrations, we exploit deep learning techniques to model the physics of aberration in an imaging system. We demonstrate the generation of a single-shot aberration-corrected field image by using a U-net-based deep neural network that learns a translation between an optical field with aberrations and an aberration-corrected field. The high fidelity of our method is demonstrated on 2D and 3D QPI measurements of various confluent eukaryotic cells, benchmarking against the conventional method using background subtractions.
Cite
@article{arxiv.2007.13038,
title = {Calibration-free quantitative phase imaging using data-driven aberration modeling},
author = {Taean Chang and Youngju Jo and Gunho Choi and Donghun Ryu and Hyun-Seok Min and Yongkeun Park},
journal= {arXiv preprint arXiv:2007.13038},
year = {2020}
}