# Phase retrieval for imaging problems

arXiv: https://arxiv.org/abs/1304.7735 · HTML: https://mlsanity.com/paper/1304.7735/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2013.

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

- [Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors](https://mlsanity.com/paper/2203.10078/) (2022)
- [Mixed Regression via Approximate Message Passing](https://mlsanity.com/paper/2304.02229/) (2023)
- [Parabolic Relaxation for Quadratically-constrained Quadratic Programming -- Part I: Definitions & Basic Properties](https://mlsanity.com/paper/2208.03622/) (2022)

## Abstract (arXiv)

We study convex relaxation algorithms for phase retrieval on imaging problems. We show that structural assumptions on the signal and the observations, such as sparsity, smoothness or positivity, can be exploited to both speed-up convergence and improve recovery performance. We detail experimental results in molecular imaging problems simulated from PDB data.