Wavefront Sensorless Aberration Correction With Magnetic Fluid Deformable Mirror for Laser Focus Control in Optical Tweezer System

Wu, Zhizheng; Zhang, Tianyu; Mbemba, Dziki; Wang, Yuanyuan; Xiang, Wei; Zhang, Zhu; Iqbal, Azhar

doi:10.1109/tmag.2020.3035723

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…The value of

, which cannot be calculated directly, should be estimated by the perturbation of the control input and related performance metric variances. In the AO system, the estimate of

can be written as [ 24 ]

where

is the amplitude of the random perturbation, and

is the independent perturbation applied to the

-th control signal.

is the change of metric caused by perturbation and can be written as the following equation when perturbed bilaterally:

…”

Section: Adaptive Coefficient Spgd Algorithm For the Lscm Systemmentioning

confidence: 99%

Active Aberration Correction with Adaptive Coefficient SPGD Algorithm for Laser Scanning Confocal Microscope

KunHua

Zhang

et al. 2022

Sensors

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A laser scanning confocal microscope (LSCM) is an effective scientific instrument for studying sub-micron structures, and it has been widely used in the field of biological detection. However, the illumination depth of LSCMs is limited due to the optical aberrations introduced by living biological tissue, which acts as an optical medium with a non-uniform refractive index, resulting in a significant dispersion of the focus of LSCM illumination light and, hence, a loss in the resolution of the image. In this study, to minimize the effect of optical aberrations, an image-based adaptive optics technology using an optimized stochastic parallel gradient descent (SPGD) algorithm with an adaptive coefficient is applied to the optical path of an LSCM system. The effectiveness of the proposed aberration correction approach is experimentally evaluated in the LSCM system. The results illustrate that the proposed adaptive optics system with an adaptive coefficient SPGD algorithm can effectively reduce the interference caused by aberrations during depth imaging.

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“…The value of

, which cannot be calculated directly, should be estimated by the perturbation of the control input and related performance metric variances. In the AO system, the estimate of

can be written as [ 24 ]

where

is the amplitude of the random perturbation, and

is the independent perturbation applied to the

-th control signal.

is the change of metric caused by perturbation and can be written as the following equation when perturbed bilaterally:

…”

Section: Adaptive Coefficient Spgd Algorithm For the Lscm Systemmentioning

confidence: 99%

Active Aberration Correction with Adaptive Coefficient SPGD Algorithm for Laser Scanning Confocal Microscope

KunHua

Zhang

et al. 2022

Sensors

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show abstract

“…However, the method typically requires the calculation and measurement of the parameters associated with the system model beforehand, which hinders its implementation as compared to the modal-free optimization algorithm. Mountain climbing, simulated annealing, augmented learning, particle swarm, genetic algorithm, and stochastic parallel gradient descent (SPGD) are among the most common modal-free algorithms [20][21][22][23][24][25]. Modal-free optimization algorithms are easier to implement because they do not rely on specific mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm

Chen

Zhou

et al. 2023

Sensors

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Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms’ convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic parallel gradient descent (SPGD) and 2SPGD algorithms in terms of convergence speed. Then, a 32-unit deformable mirror is constructed as the wavefront corrector, and the SPGD, single-dimensional perturbation descent, and 2SPSA algorithms are used in an adaptive optics numerical simulation model of the wavefront controller. Similarly, a 39-unit deformable mirror is constructed as the wavefront controller, and the SPGD and single-dimensional perturbation descent algorithms are used in an adaptive optics experimental verification device of the wavefront controller. The outcomes demonstrate that the convergence speed of the algorithm developed in this paper is more than twice as fast as that of the SPGD and 2SPGD algorithms, and the convergence accuracy of the algorithm is 4% better than that of the SPGD algorithm.

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“…However, unfavorable conditions, such as a weak target light source, an uneven light intensity distribution, and a high large light amplitude, result in a threshold value that is too low for the wavefront to interpret an accurate, leading below which the wavefront sensor extracts the effective signal leads to a significant reduction in the measurement accuracy that affects the dynamic correction performance of adaptive optics [9,10]. In contrast, a wavefront sensorless system has a higher accuracy than systems with wavefront sensors when the target light energy is weak, and their simple construction makes adaptive optics more affordable than other solutions under certain circumstances [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Optical Closed-Loop Control on the Basis of Hyperparametric Optimization of Convolutional Neural Networks

et al. 2023

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In adaptive optics systems, the precision wavefront sensor determines the closed-loop correction effect. The accuracy of the wavefront sensor is severely reduced when light energy is weak, while the real-time performance of wavefront sensorless adaptive optics systems based on iterative algorithms is poor. The wavefront correction algorithm based on deep learning can directly obtain the aberration or correction voltage from the input image light intensity data with better real-time performance. Nevertheless, manually designing deep-learning models requires a multitude of repeated experiments to adjust many hyperparameters and increase the accuracy of the system. A wavefront sensorless system based on convolutional neural networks with automatic hyperparameter optimization was proposed to address the aforementioned issues, and networks known for their superior performance, such as ResNet and DenseNet, were constructed as constructed groups. The accuracy of the model was improved by over 26%, and there were fewer parameters in the proposed method, which was more accurate and efficient according to numerical simulations and experimental validation.

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Wavefront Sensorless Aberration Correction With Magnetic Fluid Deformable Mirror for Laser Focus Control in Optical Tweezer System

Cited by 3 publications

References 23 publications

Active Aberration Correction with Adaptive Coefficient SPGD Algorithm for Laser Scanning Confocal Microscope

Active Aberration Correction with Adaptive Coefficient SPGD Algorithm for Laser Scanning Confocal Microscope

Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm

Adaptive Optical Closed-Loop Control on the Basis of Hyperparametric Optimization of Convolutional Neural Networks

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