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

arXiv: https://arxiv.org/abs/2007.13038 · HTML: https://mlsanity.com/paper/2007.13038/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements](https://mlsanity.com/paper/2103.01259/) (2021)
- [Neural Architecture Search generated Phase Retrieval Net for Real-time Off-axis Quantitative Phase Imaging](https://mlsanity.com/paper/2210.14231/) (2022)
- [DiffPR: Diffusion-Based Phase Reconstruction via Frequency-Decoupled Learning](https://mlsanity.com/paper/2506.11183/) (2025)

## Abstract (arXiv)

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.