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

TL;DR

This paper introduces a Bayesian-based iterative method for estimating phase errors from a single digital-holography data set, improving robustness against noise and large phase errors in image reconstruction.

Contribution

It presents a novel single-data realization approach for phase-error estimation using a model-based iterative algorithm within a Bayesian framework.

Findings

Robust against high noise levels

Effective with large phase errors

Accurate phase and object reflectance estimation

Abstract

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.