Calibration-free quantitative phase imaging using data-driven aberration modeling

TL;DR

This paper introduces a data-driven deep learning method for calibration-free quantitative phase imaging that corrects optical aberrations in a single shot, eliminating the need for additional calibration steps.

Contribution

It presents a novel deep neural network approach that models aberrations directly from data, enabling single-shot aberration correction without extra measurements or background regions.

Findings

Effective aberration correction demonstrated on 2D and 3D QPI data

Outperforms conventional background subtraction methods

Applicable to imaging of various confluent eukaryotic cells

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.