Digital holography wavefront sensing in the pupil-plane recording geometry for distributed-volume atmospheric aberrations

Banet, Matthias T.; Spencer, Mark F.; Raynor, Robert A.; Marker, Dan K.

doi:10.1117/12.2235462

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Using the models given by Eqs. (7)(8)(9) and (13)(14)(15)(16), along with the conditional posterior distribution of f given y and r, we can evaluate the surrogate function of Eq. (12).…”

Section: A E-step -Surrogate Function Evaluationmentioning

confidence: 99%

“…For imaging applications, we must estimate and remove these phase errors to form a focused image [2][3][4][5][6][7][8][9][10]. Similarly, for wave-front sensing applications, estimation of the phase errors represents the desired sensor output [11][12][13][14]. In either case, accurately estimating the phase errors is a necessary and critical task.…”

Section: Introductionmentioning

confidence: 99%

“…Our approach builds on the work of [15] by reconstructing the spatially-correlated reflectance, r, rather than the reflection coefficient, g, which allows for better estimates of the phase errors from less data. We focus on reconstructing from a single data realization under isoplanatic atmospheric conditions for the off-axis pupil plane recording geometry (PPRG) [13]. Our major contributions include:…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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.

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“…Using the models given by Eqs. (7)(8)(9) and (13)(14)(15)(16), along with the conditional posterior distribution of f given y and r, we can evaluate the surrogate function of Eq. (12).…”

Section: A E-step -Surrogate Function Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

2017

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Wavefront sensing requirements for high-speed AO in deep turbulence

Dave,

Ahn,

Gibson

2024

Unconventional Imaging, Sensing, and Adaptive Optics 2024

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Applications utilizing free space beam propagation over long distances in the atmosphere require active sensors and beam shaping. New wavefront sensor designs promise improved performance in deep turbulence, but comprehensive comparisons of modern wavefront sensor designs within Adaptive Optics (AO) loops have yet to reveal winning systemlevel designs for adaptive optics systems capable of correcting deep turbulence. Here, we attempt to shed light on the problem using a comprehensive wave optics model to evaluate a least-squares based and interferometric-based wavefront sensing techniques, namely the Shack-Hartmann wavefront sensor and pupil plane off-axis digital holography in combination with optimal and adaptive predictive control.The Shack Hartmann wavefront sensor has been an established wavefront sensor that provides a measurement of the wavefront through fast measurements of the wavefront gradient and least squares reconstruction. Interferometric techniques such as digital holography provide higher resolution wavefront reconstruction and improved performance with strong turbulence but with stricter laser requirements and larger computation time. For an optimal AO design in a given application, there is a trade-off between reconstructed wavefront resolution and speed. In this paper, we use waveoptics simulation to qualitatively discuss the upper bounds of AO in deep turbulence, spatial resolution limitations of Shack-Hartmann and Digital Holography wavefront sensors. We show preliminary results of closed-loop AO performance in dynamic deep turbulence, inclusive of wind and limited spatial resolution. Additionally, we show a preliminary analysis of using predictive control to improve the temporal performance of an AO loop and compensate for latencies due to hardware.

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Digital-holographic detection in the off-axis pupil plane recording geometry for deep-turbulence wavefront sensing

2018

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This paper uses wave-optics and signal-to-noise models to explore the estimation accuracy of digital-holographic detection in the off-axis pupil plane recording geometry for deep-turbulence wavefront sensing. In turn, the analysis examines three important parameters: the number of pixels across the width of the focal-plane array, the window radius in the Fourier plane, and the signal-to-noise ratio. By varying these parameters, the wave-optics and signal-to-noise models quantify performance via a metric referred to as the field-estimated Strehl ratio, and the analysis leads to a method for optimal windowing of the turbulence-limited point spread function. Altogether, 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.

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Digital holography wavefront sensing in the pupil-plane recording geometry for distributed-volume atmospheric aberrations

Cited by 4 publications

References 8 publications

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

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

Wavefront sensing requirements for high-speed AO in deep turbulence

Digital-holographic detection in the off-axis pupil plane recording geometry for deep-turbulence wavefront sensing

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