# Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging

arXiv: https://arxiv.org/abs/2009.11554 · HTML: https://mlsanity.com/paper/2009.11554/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion](https://mlsanity.com/paper/2109.07284/) (2021)
- [Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning](https://mlsanity.com/paper/2109.11452/) (2021)
- [The phase unwrapping of under-sampled interferograms using radial basis function neural networks](https://mlsanity.com/paper/2210.10541/) (2022)
- [Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction](https://mlsanity.com/paper/2604.26664/) (2026)
- [LIP-CAR: contrast agent reduction by a deep learned inverse problem](https://mlsanity.com/paper/2407.10559/) (2024)
- [Chasing Better Deep Image Priors between Over- and Under-parameterization](https://mlsanity.com/paper/2410.24187/) (2024)

## Abstract (arXiv)

Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the computational process that recovers a more informative image. It is particularly challenging with thick and complex samples such as organoids. Recent works that rely on supervised training show that deep learning is a powerful method to unwrap the phase; however, supervised approaches require large and representative datasets which are difficult to obtain for complex biological samples. Inspired by the concept of deep image priors, we propose a deep-learning-based method that does not need any training set. Our framework relies on an untrained convolutional neural network to accurately unwrap the phase while ensuring the consistency of the measurements. We experimentally demonstrate that the proposed method faithfully recovers the phase of complex samples on both real and simulated data. Our work paves the way to reliable phase imaging of thick and complex samples with QPI.