Phase-error estimation and image reconstruction from digital-holography data using a Bayesian framework
This addresses a critical bottleneck in imaging and wave-front sensing applications, offering an incremental improvement over conventional techniques.
The paper tackled the problem of estimating phase errors from digital-holography data, proposing a Bayesian method that robustly handles high noise and large phase errors from a single data realization, with results demonstrated on simulated data.
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.