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 3 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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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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Deep-turbulence wavefront sensing using digital holography in the on-axis phase shifting recording geometry with comparisons to the self-referencing interferometer

2019

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

Cited by 3 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

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

Deep-turbulence wavefront sensing using digital holography in the on-axis phase shifting recording geometry with comparisons to the self-referencing interferometer

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