Wavefront sensorless adaptive optics based on the trust region method

Yang, Qingyun; Zhao, Jinyu; Wang, Minghao; Jia, Jianlu

doi:10.1364/ol.40.001235

Cited by 23 publications

(6 citation statements)

References 8 publications

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“…年Muller等人 [4] 提出的像清晰化技术, 但早期的像清无模型优化算法主要包括遗传算法 (Genetic Algorithm, GA) [5] 、模拟退火算法 (Simulated Annealing, SA) [6] 、随机并行梯度下降法(Stochastic Parallel Gradient Descent, SPGD) [7] 、信赖域法 (Trust Region, TR) [8] 等 . 上述算法在引入随机扰动的方式上各不相同, 收敛速度也有很大差异, 研究表明收敛速度 TR>SA>SPGD>GA [8,9] . 实际应用中需要根据波前误差变化的时间特性、待优化变量个数、是否要求全局最优以及算法实现的难易程度等选择合适的算法.…”

Section: 由于传统自适应光学存在上述问题一种被称为无波前传感器自适应光学(Wavefront Sensorless Adapunclassified

Application of wavefront sensorless adaptive optics in biological imaging

Dong¹,

Wu²,

Ren³

2017

Sci. Sin.-Phys. Mech. Astron.

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Obtaining high-quality images of organisms is critical to life sciences research, but imaging optical system and organism itself contain aberrations, leading to reduced resolution and contrast. Since aberrations introduced by different organisms are different and may change with time, they cannot be compensated by conventional optical design methods. Adaptive optics, originating in astronomical imaging observations, has the ability to overcome the effects of dynamic aberrations, and can therefore be used to improve imaging quality in biological imaging. Conventional adaptive optics typically has a wavefront sensor (such as Shack-Hartmann sensor) to measure wavefront errors. Recently developed wavefront sensorless adaptive optics (WSLAO) has got rid of dedicated wavefront sensors, so the system structure can be simplified and the cost is reduced. WSLAO can also overcome some limitations of traditional adaptive optics and has good application prospects. In this paper, the working principle of WSLAO is given at first. Then the applications of this technology in fundus imaging and fluorescence microscopy are discussed and the future development trend is proposed.

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Section: 由于传统自适应光学存在上述问题一种被称为无波前传感器自适应光学(Wavefront Sensorless Adapunclassified

Application of wavefront sensorless adaptive optics in biological imaging

Dong¹,

Wu²,

Ren³

2017

Sci. Sin.-Phys. Mech. Astron.

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“…With a tested surface of large departure, there would be indistinguishable fringes even dark areas in the initial interferogram, from which the wavefront phase is unable to be demodulated. That is the FSAI usually works as a wavefront-sensorless (WFL) system [24,25]. As a WFL system, the adaptive compensation algorithm in FSAI aims to provide the optimal control signal of adaptive optics to seek near null fringes without direct aberration (or phase) information.…”

Section: Introductionmentioning

confidence: 99%

Freeform surface adaptive interferometry assisted with simulated annealing-hill climbing algorithm

Zhang

Liu

et al. 2021

Measurement

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…Finding an accurate correction without a WFS is challenging because of the nonlinearity of the underlying optimization problem. Various WFSless AO algorithms have been developed [1][2][3][4]. The common features of these methods are that they are all iterative and require many measurements to converge.…”

Section: Introductionmentioning

confidence: 99%

Predictive wavefront sensorless adaptive optics for time-varying aberrations

Piscaer¹,

Soloviev²,

Verhaegen³

2019

J. Opt. Soc. Am. A

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A new wavefront sensorless adaptive optics method is presented that can accurately correct for time-varying aberrations using a single focal plane image at each sample instance. The linear relation between the mean square of the aberration gradient and the change in second moment of the image forms the basis of the presented method. The new algorithm results in significant improvements when an accurate model of the aberration's temporal dynamics is known, by applying a Kalman filter and optimal control. Moreover, where existing wavefront sensorless adaptive optics methods update all modes sequentially, the information of the Kalman filter is used to select and update the modes that are expected to give the greatest improvement in performance. The performance is analyzed in a simulation of an adaptive optics system for atmospheric turbulence. The results show that the new method is able to correct for the aberration more accurately for higher wind speeds and higher noise levels than existing algorithms.

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Wavefront sensorless adaptive optics based on the trust region method

Cited by 23 publications

References 8 publications

Application of wavefront sensorless adaptive optics in biological imaging

Application of wavefront sensorless adaptive optics in biological imaging

Freeform surface adaptive interferometry assisted with simulated annealing-hill climbing algorithm

Predictive wavefront sensorless adaptive optics for time-varying aberrations

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