Centroid error due to non-uniform lenslet illumination in the Shack–Hartmann wavefront sensor

Akondi, Vyas; Steven, Samuel; Dubra, Alfredo

doi:10.1364/ol.44.004167

Cited by 25 publications

(3 citation statements)

References 19 publications

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“…Coherence-gated virtual Shack-Hartmann wavefront sensing (CG-vSHWS) [11,12], which is a combination of coherence-gated wavefront sensing (CGWS) and virtual SHWS (vSHWS), can overcome the above issues and provide depth-resolved wavefront information. It can also avoid the centroid errors due to inaccurate focusing [13] of the LA and non-uniform illumination [14].…”

Section: Introductionmentioning

confidence: 99%

Accounting for Polychromatic Light in Virtual Shack-Hartmann Wavefront Sensing

et al. 2023

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Virtual Shack-Hartmann wavefront sensing (vSHWS) has some significant advantages and is promising for aberration measurement in the field of biomedical optical imaging. The illumination sources used in vSHWS are almost broadband, but are treated as monochromatic sources (only using center wavelength) in current data processing, which may cause errors. This work proposed a data processing method to take into account the multiple wavelengths of the broadband spectrum, named multiple-wavelength centroid-weighting method. Its feasibility was demonstrated through a series of simulations. A wavefront generated with a set of statistical human ocular aberrations was used as the target wavefront to evaluate the performance of the proposed and current methods. The results showed that their performance was very close when used for the symmetrical, but the wavefront error of the proposed method was much smaller than that of the current method when used for the asymmetrical spectrum, especially for the broader spectrum. These results were also validated by using 20 sets of clinical human ocular aberrations including normal and diseased eyes. The proposed method and the obtained conclusions have important implications for the application of vSHWS.

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Section: Introductionmentioning

confidence: 99%

Accounting for Polychromatic Light in Virtual Shack-Hartmann Wavefront Sensing

et al. 2023

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“…For a conventional SHWFS, each subaperture has a corresponding sub-spot whose centroid displacements represent the local slop information. However, traditional centroid calculation methods [13][14][15], calculating the average positions of the intensity distribution in a specified region within the subaperture, are Mengmeng Zhao and Lingxi Kong are with Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan 610209, China; Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, invalid because of the absence of spots caused by scintillation, extended propagation paths, cloud obscuration, and nonuniform illumination [16][17][18]. Moreover, the position and number of missing sub-spots are random and dynamic [19].…”

Section: Introductionmentioning

confidence: 99%

Centroid-Predicted Deep Neural Network in Shack-Hartmann Sensors

Zhao

Wang

et al. 2022

IEEE Photonics J.

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The Shack-Hartmann wavefront sensor produces incorrect wavefront measurements when some sub-spots are weak and missing. In this paper, a method is proposed to predict the centroids of these sub-spots for the Shack-Hartmann wavefront sensor based on the deep neural network. Using the centroid information of present sub-spots, the method is able to predict the absent sub-spots' positions. The feasibility and effectiveness of this method are verified by a large number of numerical simulations. The method is applied to wavefront measurement of light with non-uniform near-field intensity. The simulation results show that the proposed method is of great help to improve the measurement accuracy and the Strehl ratio of the focal spot. For wavefronts outside of the training sample, the proposed method shows good generalization and adaptability. In addition, the experiment results demonstrate that the proposed method can predict the missing sub-spots' centroid displacements accurately even though a large proportion of sub-spot is lost randomly.

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“…(3) A real LA is not required, which can simplify the system and alignment, as well as reduce the non-common-path error. (4) The errors caused by focal shift [ 15 ] and non-uniform illumination [ 17 ] in SHWS do not present. (5) The procedure, methods, and parameters of data processing can be flexibly adjusted to achieve a high performance via optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Virtual Shack-Hartmann Wavefront Sensing

Yue

Yang

Xiao

et al. 2021

Sensors

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Virtual Shack–Hartmann wavefront sensing (vSHWS) can flexibly adjust parameters to meet different requirements without changing the system, and it is a promising means for aberration measurement. However, how to optimize its parameters to achieve the best performance is rarely discussed. In this work, the data processing procedure and methods of vSHWS were demonstrated by using a set of normal human ocular aberrations as an example. The shapes (round and square) of a virtual lenslet, the zero-padding of the sub-aperture electric field, sub-aperture number, as well as the sequences (before and after diffraction calculation), algorithms, and interval of data interpolation, were analyzed to find the optimal configuration. The effect of the above optimizations on its anti-noise performance was also studied. The Zernike coefficient errors and the root mean square of the wavefront error between the reconstructed and preset wavefronts were used for performance evaluation. The performance of the optimized vSHWS could be significantly improved compared to that of a non-optimized one, which was also verified with 20 sets of clinical human ocular aberrations. This work makes the vSHWS’s implementation clearer, and the optimization methods and the obtained results are of great significance for its applications.

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Centroid error due to non-uniform lenslet illumination in the Shack–Hartmann wavefront sensor

Cited by 25 publications

References 19 publications

Accounting for Polychromatic Light in Virtual Shack-Hartmann Wavefront Sensing

Accounting for Polychromatic Light in Virtual Shack-Hartmann Wavefront Sensing

Centroid-Predicted Deep Neural Network in Shack-Hartmann Sensors

Optimization of Virtual Shack-Hartmann Wavefront Sensing

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