The piston error recognition technique used in the modified Shack–Hartmann sensor

Li, Xiaoyang; Yang, Xu; Wang, Shengqian; Li, Bincheng; Xian, Hao

doi:10.1016/j.optcom.2021.127388

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…The correction of the piston error can usually be divided into two types: intensity techniques, in which measurements are made directly from intensity distributions or streaks, such as modified Shack-Hartmann sensing [8,9], dispersed fringes sensors [10], pyramidal sensors [11], and interferometry with masks and diffracting components [12,13]; and modulation transfer function (MTF) techniques, which can extract the peak around the MTF from piston errors, such as chromatic phase diversity (PD) [14,15], inter-segment piston sensors [16], and calibration-retrieve sensing [17,18]. When these methods are used for piston correction, complex optics need to be introduced into the optical path.…”

Section: Introductionmentioning

confidence: 99%

Piston Detection of Optical Sparse Aperture Systems Based on an Improved Phase Diversity Method

Zhao,

Li,

Liu

et al. 2023

Photonics

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The piston error has a significant effect on the imaging resolution of the optical sparse aperture system. In this paper, an improved phase diversity method based on particle swarm optimization and the sequential quadratic programming algorithm is proposed, which can overcome the drawbacks of the traditional phase diversity method and particle swarm optimization, such as the instability that results from polychromatic light conditions and premature convergence. The method introduces factor β in the stage of calculating the objective function, and combines the advantages of a heuristic algorithm and a nonlinear programming algorithm in the optimization stage, thus enhancing the accuracy and stability of piston detection. Simulations based on a dual-aperture optical sparse aperture system verified that the root mean square error obtained by the method can be guaranteed to be within 0.001λ (wavelength), which satisfies the requirement of practical imaging. An experimental test was also conducted to demonstrate the performance of the method, and the test results showed that the quality of the image after piston detection and correction improved significantly compared to images with the co-phase error.

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Section: Introductionmentioning

confidence: 99%

Piston Detection of Optical Sparse Aperture Systems Based on an Improved Phase Diversity Method

Zhao,

Li,

Liu

et al. 2023

Photonics

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show abstract

“…Many methods have been used for the piston error detection of the OSA system, including the Schack Hartmann sensor, which can detect the piston error between the sub-mirrors of the micro-lens array [ 6 ]; however, the low sensing accuracy of the Schack Hartmann sensor only satisfies coarse phasing with a limited detection range, and suffers 2

ambiguity. Another mature approach, Phase Diversity (PD), extracts the piston through a pair of focused and defocused images: it breaks the limits of the imaging content, and could be applied to the expanded target [ 7 ]; however, Phase Diversity (PD) is cumbersome, so the calculations waste much time, and the 2π ambiguity also affects this method.…”

Section: Introductionmentioning

confidence: 99%

Piston Sensing for Golay-6 Sparse Aperture System with Double-Defocused Sharpness Metrics via ResNet-34

Wang

Fan

et al. 2022

Sensors

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In pursuit of high imaging quality, optical sparse aperture systems must correct piston errors quickly within a small range. In this paper, we modified the existing deep-learning piston detection method for the Golay-6 array, by using a more powerful single convolutional neural network based on ResNet-34 for feature extraction; another fully connected layer was added, on the basis of this network, to obtain the best results. The Double-defocused Sharpness Metric (DSM) was selected first, as a feature vector to enhance the model performance; the average RMSE of the five sub-apertures for valid detection in our study was only 0.015λ (9 nm). This modified method has higher detecting precision, and requires fewer training datasets with less training time. Compared to the conventional approach, this technique is more suitable for the piston sensing of complex configurations.

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Super-resolution macroscopic imaging via unknown speckle illumination using sparse aperture transmitter

Tang,

Wei,

Xie

et al. 2024

Optics Communications

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The piston error recognition technique used in the modified Shack–Hartmann sensor

Cited by 4 publications

References 24 publications

Piston Detection of Optical Sparse Aperture Systems Based on an Improved Phase Diversity Method

Piston Detection of Optical Sparse Aperture Systems Based on an Improved Phase Diversity Method

Piston Sensing for Golay-6 Sparse Aperture System with Double-Defocused Sharpness Metrics via ResNet-34

Super-resolution macroscopic imaging via unknown speckle illumination using sparse aperture transmitter

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