Coherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around 2007. Wavefront shaping is aimed at overcoming the strong sc...

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Artificial intelligence-assisted light control and computational imaging through scattering media

Adaptive optics relies on the fast and accurate determination of aberrations but is often hindered by wavefront sensor limitations or lengthy optimization algorithms. Deep learning by artificial neural networks has recently been shown to provide determination of aberration coefficients from various microscope metrics. Here we numerically investigate the direct determination of aberration functions in the pupil plane of a high numerical aperture microscope using an artificial neural network. We show that an aberration function can be determined from fluorescent guide stars and used to improve the Strehl ratio without the need for reconstruction from Zernike polynomial coefficients.

Direct determination of aberration functions in microscopy by an artificial neural network

Adaptive optics techniques have been developed over the past half century and routinely used in large ground-based telescopes for more than 30 years. Although this technique has already been used in various applications, the basic setup and methods have not changed over the past 40 years. In recent years, with the rapid development of artificial intelligence, adaptive optics will be boosted dramatically. In this paper, the recent advances on almost all aspects of adaptive optics based on machine learning are summarized. The state-of-the-art performance of intelligent adaptive optics are reviewed. The potential advantages and deficiencies of intelligent adaptive optics are also discussed.

https://www.oejournal.org/data/article/export-pdf?id=61f370bc99d881528090e6c8

Adaptive optics based on machine learning: a review

Deep learning neural networks are used for wavefront sensing and aberration correction in atmospheric turbulence without any wavefront sensor (i.e. reconstruction of the wavefront aberration phase from the distorted image of the object). We compared and found the characteristics of the direct and indirect reconstruction ways: (i) directly reconstructing the aberration phase; (ii) reconstructing the Zernike coefficients and then calculating the aberration phase. We verified the generalization ability and performance of the network for a single object and multiple objects. What’s more, we verified the correction effect for a turbulence pool and the feasibility for a real atmospheric turbulence environment.

/pdf/deep-learning-wavefront-sensing-and-aberration-correction-in-23lh1716y7.pdf

Deep learning wavefront sensing and aberration correction in atmospheric turbulence

We propose a convolutional neural network (CNN) based method, namely phase diversity convolutional neural network (PD-CNN) for the speed acceleration of phase-diversity wavefront sensing. The PD-CNN has achieved a state-of-the-art result, with the inference speed about 0.5 ms, while fusing the information of the focal and defocused intensity images. When compared to the traditional phase diversity (PD) algorithms, the PD-CNN is a light-weight model without complicated iterative transformation and optimization process. Experiments have been done to demonstrate the accuracy and speed of the proposed approach.

https://www.mdpi.com/1424-8220/20/17/4877/pdf

Sub-Millisecond Phase Retrieval for Phase-Diversity Wavefront Sensor

Numerical study of adaptive optics compensation based on Convolutional Neural Networks

While the entangled orbital angular momentum (OAM) photons propagate through the turbulent atmosphere, the OAM entanglement is decoherent. Adaptive optics (AO) system is an effective technique to mitigate wavefront distortion induced by atmospheric turbulence. In this paper, we investigate the performance of the system when the entangled OAM photons propagate though atmospheric turbulence with AO system compensation. The influence of reconstruction algorithm and spatial bandwidth of corrector is analyzed. The results show that branch-point (BP) algorithm has better compensation performance when Rytov number > 0.2 than the least mean square error (LS) algorithm. High spatial bandwidth has better compensation performance.

Numerical study of orbital angular momentum-entanglement in turbulence with adaptive optics system compensation

Investigation of noise amplification questions in satellite jitter detected from CCDs’ parallax observation imagery: A case for 3 CCDs

Adaptive optics (AO) is an effective method to compensate the wavefront distortion caused by atmospheric turbulence and system distortion. The accuracy and speed of aberration restoration are important factors affecting the performance of adaptive optics correction. In recent years, an AO correction method based on a convolutional neural network (CNN) has been proposed for the non-iterative extraction of light intensity image features and recovery of phase information. This method can directly predict the Zernike coefficient of the wavefront from the measured light intensity image and effectively improve the real-time correction ability of the AO system. In this paper, a turbulence aberration restoration based on two frames of a light intensity image using GoogLeNet is established. Three depth scales of GoogLeNet and different amounts of data training are tested to verify the accuracy of Zernike phase difference restoration at different turbulence intensities. The results show that the training of small data sets easily overfits the data, while the training performance of large data sets is more stable and requires a deeper network, which is conducive to improving the accuracy of turbulence aberration restoration. The restoration effect of third-order to seventh-order aberrations is significant under different turbulence intensities. With the increase in the Zernike coefficient, the error increases gradually. However, there are valley points lower than the previous growth for the 10th-, 15th-, 16th-, 21st-, 28th- and 29th-order aberrations. For higher-order aberrations, the greater the turbulence intensity, the greater the restoration error. The research content of this paper can provide a network design reference for turbulence aberration restoration based on deep learning.

https://www.mdpi.com/2304-6732/10/3/265/pdf?version=1677806273

Haiqiu Liu

Papers

Numerical study of adaptive optics compensation based on Convolutional Neural Networks

Numerical study of orbital angular momentum-entanglement in turbulence with adaptive optics system compensation

Investigation of noise amplification questions in satellite jitter detected from CCDs’ parallax observation imagery: A case for 3 CCDs

Turbulence Aberration Restoration Based on Light Intensity Image Using GoogLeNet

Image motion compensation method based on large elliptical orbit