Wavefront sensing in imaging through the atmosphere: a detector strategy

Sechaud, Marc; Rousset, G.; Michau, Vincent; Fontanella, J. C.; Cuby, J. G.; Rigaut, François; Richard, J. C.

doi:10.1117/12.51203

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…The angle-of-arrival temporal power spectrum for an aperture ofdiameter d has a -11/3 asymptotic power law beyond the cut-offfrequency f = 0.3 F/d. 8 The asymptotic temporal undersampling error a is proportional to the phase variance over a subaperture and to the ratio of the cut-off frequency over the sampling frequency f8 to the 8/3 power.9 It may be written: (6) To imaging Js 2.1.3. Adaptive optics system optimization Finally, the total wave-front phase error variance a, may be written:…”

Section: Undersampling Errorsmentioning

confidence: 99%

<title>Adaptive optics ultimate performance and main concepts</title>

Sechaud

1998

SPIE Proceedings

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Adaptive optics is now an operational reality which provides a real time compensation for turbulence degraded images. The degree of correction of an adaptive optics system is fundamentally limited by the flux density received from the object or from a guide source, and by atmospheric turbulence strength and speed. The ultimate performance which can be expected for ideal systems with an optimization of the key parameters are estimated. Two types of systems are routinely operated to date: a Hartmann-Shack wave-front sensor with a discrete actuator deformable mirror and a curvature wave-front sensor with a bimorph mirror. They are compared from physical and technological standpoints.

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Section: Undersampling Errorsmentioning

confidence: 99%

<title>Adaptive optics ultimate performance and main concepts</title>

Sechaud

1998

SPIE Proceedings

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“…Matched filter [2], minimum mean-square-error estimator [3], and maximum-likelihood methods [4] have been studied to estimate the centroid or wavefront slope as well. If the source is an extended object instead of a point source, cross-correlation methods can also be used to estimate the sub-image displacement [5][6][7]. A comparison of some commonly used centroiding algorithms, including thresholding (TCoG), weighted centroid (WCoG), correlation, and quad cell, is given by Thomas et al [8].…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of Shack–Hartmann Wavefront Sensing Using Stream-Based Center of Gravity Method for Centroid Estimation

2023

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We present a fast and reconfigurable architecture for Shack–Hartmann wavefront sensing implemented on FPGA devices using a stream-based center of gravity to measure the spot displacements. By calculating the center of gravity around each incoming pixel with an optimal window matching the spot size, the common trade-off between noise and bias errors and dynamic range due to window size existing in conventional center of gravity methods is avoided. In addition, the accuracy of centroid estimation is not compromised when the spot moves to or even crosses the sub-aperture boundary, leading to an increased dynamic range. The calculation of the centroid begins while the pixel values are read from an image sensor and further computation such as slope and partial wavefront reconstruction follows immediately as the sub-aperture centroids are ready. The result is a real-time wavefront sensing system with very low latency and high measurement accuracy feasible for targeting on low-cost FPGA devices. This architecture provides a promising solution which can cope with multiple target objects and work in moderate scintillation.

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“…In these systems, wavefront sensors play an essential role. There are several types of wavefront sensors, e.g., Shack-Hartmann sensors (Shack and Platt 1971;Noguchi et al 1989;Séchaud et al 1991), shearing interferometers (Koliopoulos 1980;Hardy and MacGouem 1987), curvature sensors (Roddier et al 1988), and so on. Among others, Shack-Hartmann sensors have been the most widely used for astronomical applications.…”

Section: Introductionmentioning

confidence: 99%

Wavefront reconstruction error of Shack-Hartmann wavefront sensors

Takato¹,

Iye²,

Yamaguchi³

1994

PASP

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We have evaluated the wavefront reconstruction error for Shack-Hartmann wavefront sensors based on a series of simulations for various numbers of subapertures at various levels of measurement error in determining the Hartmann spot centroids. The optimum number of subapertures is derived for a given magnitude reference star in the cases of photon-noise-limited and detector-noise-limited sensors. When the centroid measuring error is absent, the maximum number of Zemike terms that can be corrected by means of modal adaptive compensation is about 1/5 of the number of subapertures. The residual phase error in wavefront reconstruction is shown to be sensitive to the measuring error of the spot centroids. It is essential to reduce the centroid measuring error in wavefront sensors for high-level adaptive optics systems with a large number of degrees of freedom.

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Wavefront sensing in imaging through the atmosphere: a detector strategy

Abstract: Wavefront sensing is a very powerful technique whose capability in the field of diffraction-limited imaging through turbulence has been demonstrated. The ultimate performance of a Hartmann-Shack wavefront sensor is analysed and used to define a detector choice strategy.

Cited by 5 publications

References 5 publications

<title>Adaptive optics ultimate performance and main concepts</title>

<title>Adaptive optics ultimate performance and main concepts</title>

FPGA Implementation of Shack–Hartmann Wavefront Sensing Using Stream-Based Center of Gravity Method for Centroid Estimation

Wavefront reconstruction error of Shack-Hartmann wavefront sensors

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