# Phase-error estimation and image reconstruction from digital-holography data using a Bayesian framework

arXiv: https://arxiv.org/abs/1708.01142 · HTML: https://mlsanity.com/paper/1708.01142/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2017.

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

- [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)
- [Maximum Likelihood Reconstruction for Multi-Look Digital Holography with Markov-Modeled Speckle Correlation](https://mlsanity.com/paper/2604.20154/) (2026)
- [Monte Carlo Maximum Likelihood Reconstruction for Digital Holography with Speckle](https://mlsanity.com/paper/2602.10344/) (2026)
- [Implicit Neural Speckle Denoising](https://mlsanity.com/paper/2608.06574/) (2026)

## Abstract (arXiv)

The estimation of phase errors from digital-holography data is critical for applications such as imaging or wave-front sensing. Conventional techniques require multiple i.i.d. data and perform poorly in the presence of high noise or large phase errors. In this paper we propose a method to estimate isoplanatic phase errors from a single data realization. We develop a model-based iterative reconstruction algorithm which computes the maximum a posteriori estimate of the phase and the speckle-free object reflectance. Using simulated data, we show that the algorithm is robust against high noise and strong phase errors.