# Robust Compressive Phase Retrieval via Deep Generative Priors

arXiv: https://arxiv.org/abs/1808.05854 · HTML: https://mlsanity.com/paper/1808.05854/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [On the use of deep learning for phase recovery](https://mlsanity.com/paper/2308.00942/) (2023)
- [Makeup-Guided Facial Privacy Protection via Untrained Neural Network Priors](https://mlsanity.com/paper/2408.12387/) (2024)
- [Compressive Ptychography using Deep Image and Generative Priors](https://mlsanity.com/paper/2205.02397/) (2022)
- [A Deep Generative Approach to Oversampling in Ptychography](https://mlsanity.com/paper/2207.14392/) (2022)
- [Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime](https://mlsanity.com/paper/2411.18189/) (2024)

## Abstract (arXiv)

This paper proposes a new framework to regularize the highly ill-posed and non-linear phase retrieval problem through deep generative priors using simple gradient descent algorithm. We experimentally show effectiveness of proposed algorithm for random Gaussian measurements (practically relevant in imaging through scattering media) and Fourier friendly measurements (relevant in optical set ups). We demonstrate that proposed approach achieves impressive results when compared with traditional hand engineered priors including sparsity and denoising frameworks for number of measurements and robustness against noise. Finally, we show the effectiveness of the proposed approach on a real transmission matrix dataset in an actual application of multiple scattering media imaging.