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

The estimation of phase errors from digital-holography data is critical for applications such as imaging or wavefront 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 that 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.