Shack-Hartmann spot dislocation map determination using an optical flow method

Vargas, Javier; Restrepo, René; Belenguer, T.

doi:10.1364/oe.22.001319

Cited by 19 publications

(3 citation statements)

References 16 publications

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“…To date, the research on Shack-Hartmann wavefront sensors has primarily focused on point-target imaging [8][9][10]. Various techniques have been employed to enhance the accuracy of the centroid extraction, including the windowed thresholded centroid of mass method [11], local adaptive thresholding [12,13], and the autocorrelation centroid of mass extraction [14].…”

Section: Introductionmentioning

confidence: 99%

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Chen,

Jia,

Zhou

et al. 2023

Applied Sciences

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Due to the influence of atmospheric turbulence, the detector, and background noise, the subaperture image of an extended scene Shack–Hartmann wavefront sensor will have a low signal-to-noise ratio, which will introduce errors to the offset estimation and reduce the accuracy of the slope measurement. To solve this problem, this paper proposes a cross-correlation subaperture image preprocessing method, which uses the generalized Anscombe transform to convert the Gauss–Poisson noise into Gaussian noise and introduces residual feedback on the basis of BM3D to achieve the efficient denoising of subaperture images. The simulation results show that compared with the three commonly used denoising algorithms, the proposed method improves the relative error of the subaperture offset calculation by 51.96% and the corresponding Zernike coefficient of distorted reconstruction wavefront by 85.56%, which realizes the improvement in the detection accuracy on the basis of effectively retaining image details.

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Section: Introductionmentioning

confidence: 99%

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Chen,

Jia,

Zhou

et al. 2023

Applied Sciences

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“…To date, a large number of studies have focused on improving the center of gravity (CoG) method [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], but, if the aberration to be measured is too large and the fluctuation of the wavefront is too steep, it will cause some sub-aperture spots to deviate from the corresponding sub-aperture. This condition is commonly measured in patients with severe refractive errors and patients who have undergone corneal or lens surgery [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Method Used to Improve the Dynamic Range of Shack–Hartmann Wavefront Sensor in Presence of Large Aberration

Yang

Wang

2022

Sensors

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With the successful application of the Shack–Hartmann wavefront sensor in measuring aberrations of the human eye, researchers found that, when the aberration is large, the local wavefront distortion is large, and it causes the spot corresponding to the sub-aperture of the microlens to shift out of the corresponding range of the sub-aperture. However, the traditional wavefront reconstruction algorithm searches for the spot within the corresponding range of the sub-aperture of the microlens and reconstructs the wavefront according to the calculated centroid, which leads to wavefront reconstruction errors. To solve the problem of the small dynamic range of the Shack–Hartmann wavefront sensor, this paper proposes a wavefront reconstruction algorithm based on the autocorrelation method and a neural network. The autocorrelation centroid extraction method was used to calculate the centroid in the entire spot map in order to obtain a centroid map and to reconstruct the wavefront by matching the centroid with the microlens array through the neural network. This method breaks the limitation of the sub-aperture of the microlens. The experimental results show that the algorithm improves the dynamic range of the first 15 terms of the Zernike aberration reconstruction to varying degrees, ranging from 62.86% to 183.87%.

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“…The most important algorithms used here are: center of mass [14], weighted center of gravity [15], the iteratively weighted centroiding [16], matched filter approaches [17], and spiral phase transform [18]. Knowing many local Δx and Δy values, we can estimate the phase aberration with the use of a number of algorithms, such as: the zonal reconstruction method [19,20], phase-retrieval or modal reconstruction method [21], methods based on the fast Fourier transform [22], multigrid [23], wavelet-based [24], fractal iterative [25], cumulative reconstructor [26], spline-based [27], non-linear intensity phase retrieval [28] and methods based on optical flow [29]. For a better reconstruction of the wavefront distortion, some of these methods additionally apply light distribution on the spots.…”

Section: Introductionmentioning

confidence: 99%

High resolution Shack-Hartmann sensor based on array of nanostructured GRIN lenses

Kasztelanic¹,

Filipkowski²,

Pysz³

et al. 2017

Opt. Express

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We present a novel method for the development of a micro lenslets hexagonal array. We use gradient index (GRIN) micro lenses where the variation of the refraction index is achieved with a structure of nanorods made of 2 types of glasses. To develop the GRIN micro lens array, we used a modified stack-and-draw technology which was originally applied for the fabrication of photonic crystal fibers. This approach results in a completely flat element that is easy to integrate with other optical components and can be effectively used in high refractive index medium as liquids. As a proof-of-concept of the method we present a hexagonal array of 469 GRIN micro lenses with a diameter of 20 µm each and 100% fill factor. The GRIN lens array is further used to build a Shack-Hartmann detector for measuring wavefront distortion. A 50 lens/mm sampling density is achieved.

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Shack-Hartmann spot dislocation map determination using an optical flow method

Cited by 19 publications

References 16 publications

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

Expanded Scene Image Preprocessing Method for the Shack–Hartmann Wavefront Sensor

A Method Used to Improve the Dynamic Range of Shack–Hartmann Wavefront Sensor in Presence of Large Aberration

High resolution Shack-Hartmann sensor based on array of nanostructured GRIN lenses

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