# Practical Phase Retrieval Using Double Deep Image Priors

arXiv: https://arxiv.org/abs/2211.00799 · HTML: https://mlsanity.com/paper/2211.00799/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 56 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 23 of 23)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- ["Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors](https://mlsanity.com/paper/1812.00467/) (2018)
- [prDeep: Robust Phase Retrieval with a Flexible Deep Network](https://mlsanity.com/paper/1803.00212/) (2018)
- [Lensless computational imaging through deep learning](https://mlsanity.com/paper/1702.08516/) (2017)
- [Early Stopping for Deep Image Prior](https://mlsanity.com/paper/2112.06074/) (2021)
- [Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators](https://mlsanity.com/paper/1910.14634/) (2019)
- [Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation](https://mlsanity.com/paper/2005.03991/) (2020)
- [The Numerics of Phase Retrieval](https://mlsanity.com/paper/2004.05788/) (2020)
- [AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale Bragg Coherent Diffraction Imaging](https://mlsanity.com/paper/2109.14053/) (2021)
- [Real-time coherent diffraction inversion using deep generative networks](https://mlsanity.com/paper/1806.03992/) (2018)
- [PhaseGAN: A deep-learning phase-retrieval approach for unpaired datasets](https://mlsanity.com/paper/2011.08660/) (2020)
- [Self-Validation: Early Stopping for Single-Instance Deep Generative Priors](https://mlsanity.com/paper/2110.12271/) (2021)
- [Experimental robustness of Fourier Ptychography phase retrieval algorithms](https://mlsanity.com/paper/1511.02986/) (2015)
- [Deep Iterative Reconstruction for Phase Retrieval](https://mlsanity.com/paper/1904.11301/) (2019)
- [Phase Retrieval Using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1912.04981/) (2019)
- [Three-dimensional Coherent X-ray Diffraction Imaging via Deep Convolutional Neural Networks](https://mlsanity.com/paper/2103.00001/) (2021)
- [Blind Image Deblurring with Unknown Kernel Size and Substantial Noise](https://mlsanity.com/paper/2208.09483/) (2022)
- [Inverse Problems, Deep Learning, and Symmetry Breaking](https://mlsanity.com/paper/2003.09077/) (2020)
- [Adaptive 3D convolutional neural network-based reconstruction method for 3D coherent diffraction imaging](https://mlsanity.com/paper/2008.10094/) (2020)
- [When deep denoising meets iterative phase retrieval](https://mlsanity.com/paper/2003.01792/) (2020)
- [Phase Retrieval using Single-Instance Deep Generative Prior](https://mlsanity.com/paper/2106.04812/) (2021)
- [Towards Low-Photon Nanoscale Imaging: Holographic Phase Retrieval via Maximum Likelihood Optimization](https://mlsanity.com/paper/2105.11512/) (2021)
- [Optimization on Spheres: Models and Proximal Algorithms with Computational Performance Comparisons](https://mlsanity.com/paper/1810.02893/) (2018)

## Built on by (most cited first, 10 of 10)

- [A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior](https://mlsanity.com/paper/2502.13998/) (2025)
- [JoIN: Joint GANs Inversion for Intrinsic Image Decomposition](https://mlsanity.com/paper/2305.11321/) (2023)
- [Bagged Deep Image Prior for Recovering Images in the Presence of Speckle Noise](https://mlsanity.com/paper/2402.15635/) (2024)
- [Multilook Coherent Imaging: Theoretical Guarantees and Algorithms](https://mlsanity.com/paper/2505.23594/) (2025)
- [FMPlug: Plug-In Foundation Flow-Matching Priors for Inverse Problems](https://mlsanity.com/paper/2508.00721/) (2025)
- [Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior](https://mlsanity.com/paper/2305.07712/) (2023)
- [Acceleration and Implicit Regularization in Gaussian Phase Retrieval](https://mlsanity.com/paper/2311.12888/) (2023)
- [Low-light phase retrieval with implicit generative priors](https://mlsanity.com/paper/2402.17745/) (2024)
- [Coordinate-based Neural Network for Fourier Phase Retrieval](https://mlsanity.com/paper/2311.14925/) (2023)
- [Saving Foundation Flow-Matching Priors for Inverse Problems](https://mlsanity.com/paper/2511.16520/) (2025)

## Abstract (arXiv)

Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes. We identify the connection between the difficulty level and the number and variety of symmetries in PR problems. We focus on the most difficult far-field PR (FFPR), and propose a novel method using double deep image priors. In realistic evaluation, our method outperforms all competing methods by large margins. As a single-instance method, our method requires no training data and minimal hyperparameter tuning, and hence enjoys good practicality.