Mark F. Spencer

TL;DR: In this article, the estimation accuracy of digital-holographic detection for wavefront sensing in the presence of distributed volume or "deep" turbulence and detection noise is investigated, where the authors develop wave-optics simulations which explore the estimation accuracies of digital holographic detection.

TL;DR: This paper develops a model-based iterative reconstruction algorithm that computes the maximum a posteriori estimate of the phase and the speckle-free object reflectance and shows that the algorithm is robust against high noise and strong phase errors.

TL;DR: This paper explores the use of single-shot digital holography data and a novel algorithm, referred to as multiplane iterative reconstruction (MIR), for imaging through distributed-volume aberrations and shows that the MIR algorithm outperforms the leading multiplane image-sharpening algorithm over a wide range of anisoplanatic conditions.

TL;DR: The results show the four-step method is the most efficient phase-shifting strategy and deep-turbulence conditions only degrade performance with respect to insufficient focal-plane array sampling and low signal-to-noise ratios.

TL;DR: The results will allow future research efforts to assess the number of pixels, pixel size, pixel-well depth, and read-noise standard deviation needed from a focal-plane array when using digital-holographic detection in the off-axis pupil plane recording geometry for estimating the complex-optical field when in the presence of deep turbulence and detection noise.