Deep-turbulence wavefront sensing using digital-holographic detection in the off-axis image plane recording geometry

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

doi:10.1117/1.oe.56.3.031213

Cited by 70 publications

(52 citation statements)

References 31 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%

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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%

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

2017

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“…The resulting data are sensitive to phase errors caused by index-of-refraction perturbations in, for example, the atmosphere or optical systems. With this in mind, we can estimate these phase errors directly from the DH data for wavefront sensing purposes or to digitally correct aberrated images [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Imaging through distributed-volume aberrations using single-shot digital holography

2019

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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. Such aberrations result in a linear, shift-varying or "anisoplanatic" physical process, where multiple-look angles give rise to different point spread functions within the field of view of the imaging system. The MIR algorithm jointly computes the maximum a posteriori estimates of the anisoplanatic phase errors and the speckle-free object reflectance from the single-shot digital holography data. Using both simulations and experiments, we show that the MIR algorithm outperforms the leading multiplane image-sharpening algorithm over a wide range of anisoplanatic conditions.

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“…This off-axis image plane recording geometry [28] is extremely common among wave propagation simulations. First, it is assumed that the object is uniformly illuminated with the on-axis monochromatic light from a master-oscillator laser, which has a wavelength of λ.…”

Section: Simulation Setupmentioning

confidence: 99%

Predictive dynamic digital holography

2016

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Digital holography has received recent attention for many imaging and sensing applications, including imaging through turbulent and turbid media, adaptive optics, three-dimensional projective display technology and optical tweezing. It holds several advantages over classical methods for wavefront sensing and adaptive-optics correction, chief among these being significantly fewer and simpler optical components. A significant obstacle for digital holography in real-time applications, such as wavefront sensing for high-energy laser systems and high-speed imaging for target tracking, is the fact that digital holography is computationally intensive; it requires iterative virtual wavefront propagation and hill-climbing algorithms to optimize sharpness criteria. This research demonstrates real-time methods for digital holography based on approaches for optimal and adaptive identification, prediction, and control of optical wavefronts. The methods presented integrate minimum-variance wavefront prediction into dynamic digital holography schemes to accelerate the wavefront correction and image sharpening algorithms. Further gains in computational efficiency are demonstrated in this work with a variant of localized sharpening in conjunction with predictive dynamic digital holography for real-time applications. This "subspace correction" method optimizes sharpness of local regions in a detector plane by parallel independent wavefront correction on reduced-dimension subspaces of the complex field in a spectral plane.ii The dissertation of Sennan David Sulaiman is approved.

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Deep-turbulence wavefront sensing using digital-holographic detection in the off-axis image plane recording geometry

Cited by 70 publications

References 31 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

Imaging through distributed-volume aberrations using single-shot digital holography

Predictive dynamic digital holography

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