Adaptive optics based on machine learning: a review

Guo, Youming; Zhong, Lei; Lei, Min; Wang, Jiaying; Wu, Yu Cheng; Chen, Kele; Wei, Kai; Rao, Changhui

doi:10.29026/oea.2022.200082

Cited by 95 publications

(45 citation statements)

References 96 publications

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“…This technology can detect the aberrations introduced by the atmosphere and correct them using a deformable mirror. Nowadays, the AO-based technology has found extensive applications in optical imaging [38,39], particularly in confocal and multiphoton microscopy, as it facilitates three-dimensional volumetric imaging of thick biospecimens [40,41]. One major drawback of the AO technology is the need for additional hardware, such as Shack-Hartman wavefront sensor, spatial light modulator, and deformable mirror.…”

Section: Introductionmentioning

confidence: 99%

Adaptive optical quantitative phase imaging based on annular illumination Fourier ptychographic microscopy

et al. 2022

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Quantitative phase imaging (QPI) has emerged as a valuable tool for biomedical research thanks to its unique capabilities for quantifying optical thickness variation of living cells and tissues. Among many QPI methods, Fourier ptychographic microscopy (FPM) allows long-term label-free observation and quantitative analysis of large cell populations without compromising spatial and temporal resolution. However, high spatio-temporal resolution imaging over a long-time scale (from hours to days) remains a critical challenge: optically inhomogeneous structure of biological specimens as well as mechanical perturbations and thermal fluctuations of the microscope body all result in time-varying aberration and focus drifts, significantly degrading the imaging performance for long-term study. Moreover, the aberrations are sample- and environment-dependent, and cannot be compensated by a fixed optical design, thus necessitating rapid dynamic correction in the imaging process. Here, we report an adaptive optical QPI method based on annular illumination FPM. In this method, the annular matched illumination configuration (i.e., the illumination numerical aperture (NA) strictly equals to the objective NA), which is the key for recovering low-frequency phase information, is further utilized for the accurate imaging aberration characterization. By using only 6 low-resolution images captured with 6 different illumination angles matching the NA of a 10x, 0.4 NA objective, we recover high-resolution quantitative phase images (synthetic NA of 0.8) and characterize the aberrations in real time, restoring the optimum resolution of the system adaptively. Applying our method to live-cell imaging, we achieve diffraction-limited performance (full-pitch resolution of $$655\,nm$$ 655 n m at a wavelength of $$525\,nm$$ 525 n m ) across a wide field of view ($$1.77\,mm^2$$ 1.77 m m 2 ) over an extended period of time.

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Section: Introductionmentioning

confidence: 99%

Adaptive optical quantitative phase imaging based on annular illumination Fourier ptychographic microscopy

et al. 2022

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“…There are just a few known areas where some of this concept have been implemented. For example, the optimization of adaptive optics systems has subject of deep learning applications (see [100] for a recent review). Many works are exploring the design of optical element configurations with deep neural networks and similarly flexible algorithms [101][102][103][104][105].…”

Section: Experiments and Instrument Designmentioning

confidence: 99%

Machine Learning and Cosmology

Dvorkin,

Mishra-Sharma,

Nord

et al. 2022

Preprint

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Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educational pathways have emerged. Despite rapid progress, substantial potential at the intersection of cosmology and machine learning remains untapped. In this white paper, we summarize current and ongoing developments relating to the application of machine learning within cosmology and provide a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities.

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“…There are numerous papers (see, for example, [11,12] and literature in these papers) dedicated to coherent beam combining using DNN with offline training strategy. Methods of DNN utilization for syntheses of optimal control are significantly varied for different authors and include direct generation of control by DNN using target-plane measurements [13], generation by DNN of some initial pupil-plane phase distributions [14] and more sophisticated cascaded schemes where DNN is operated in pair with SPGD [15].…”

Section: Pistons Controlmentioning

confidence: 99%

The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system

Воронцов¹,

Филимонов²

2022

Preprint

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In this study we consider adaptive power beaming with fiber-array laser transmitter system in presence of atmospheric turbulence. For optimization of power transition through the atmosphere fiberarray is traditionally controlled by stochastic parallel gradient descent (SPGD) algorithm where control feedback is provided via radio frequency link by an optical-to-electrical power conversion sensor, attached to a cooperative target. The SPGD algorithm continuously and randomly perturbs voltages applied to fiber-array phase shifters and fiber tip positioners in order to maximize sensor signal, i.e. uses, so-called, "blind" optimization principle. In opposite to this approach a perspective artificially intelligent (AI) control systems for synthesis of optimal control can utilize various pupil-or target-plane data available for the analysis including wavefront sensor data, photo-voltaic array (PVA) data, other optical or atmospheric parameters, and potentially can eliminate well-known drawbacks of SPGD-based controllers. In this study an optimal control is synthesized by a deep neural network (DNN) using target-plane PVA sensor data as its input. A DNN training is occurred online in sync with control system operation and is performed by applying of small perturbations to DNN's outputs. This approach does not require initial DNN's pre-training as well as guarantees optimization of system performance in time. All theoretical results are verified by numerical experiments.

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Adaptive optics based on machine learning: a review

Cited by 95 publications

References 96 publications

Adaptive optical quantitative phase imaging based on annular illumination Fourier ptychographic microscopy

Adaptive optical quantitative phase imaging based on annular illumination Fourier ptychographic microscopy

Machine Learning and Cosmology

The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system

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