Sub-Millisecond Phase Retrieval for Phase-Diversity Wavefront Sensor

Wu, Yu Cheng; Guo, Youming; Bao, Hua; Rao, Changhui

doi:10.3390/s20174877

Cited by 28 publications

(16 citation statements)

References 30 publications

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“…Naik et al (2020) used a compact CNN for object-agnostic wavefront sensing, inferring up to six Zernike coefficients, but reported a poorly sensed coma. Wu et al (2020) trained a CNN for fast inference of 13 Zernike coefficients and obtained mild improvements for input aberrations of around 2 rad root mean square (rms). Nishizaki et al (2019) proposed to extend the design space of wavefront sensor using deep learning where the inputs are preconditioned images such as overexposed, defocused, or scattered images.…”

mentioning

confidence: 99%

Focal plane wavefront sensing using machine learning: performance of convolutional neural networks compared to fundamental limits

Xivry

Quesnel

Vanberg

et al. 2021

Monthly Notices of the Royal Astronomical Society

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Focal plane wavefront sensing (FPWFS) is appealing for several reasons. Notably, it offers high sensitivity and does not suffer from non-common path aberrations (NCPA). The price to pay is a high computational burden and the need for diversity to lift any phase ambiguity. If those limitations can be overcome, FPWFS is a great solution for NCPA measurement, a key limitation for high-contrast imaging, and could be used as adaptive optics wavefront sensor. Here, we propose to use deep convolutional neural networks (CNNs) to measure NCPA based on focal plane images. Two CNN architectures are considered: ResNet-50 and U-Net which are used respectively to estimate Zernike coefficients or directly the phase. The models are trained on labelled datasets and evaluated at various flux levels and for two spatial frequency contents (20 and 100 Zernike modes). In these idealized simulations we demonstrate that the CNN-based models reach the photon noise limit in a large range of conditions. We show, for example, that the root mean squared (rms) wavefront error (WFE) can be reduced to <λ/1500 for 2 × 106 photons in one iteration when estimating 20 Zernike modes. We also show that CNN-based models are sufficiently robust to varying signal-to-noise ratio, under the presence of higher-order aberrations, and under different amplitudes of aberrations. Additionally, they display similar to superior performance compared to iterative phase retrieval algorithms. CNNs therefore represent a compelling way to implement FPWFS, which can leverage the high sensitivity of FPWFS over a broad range of conditions.

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mentioning

confidence: 99%

Focal plane wavefront sensing using machine learning: performance of convolutional neural networks compared to fundamental limits

Xivry

Quesnel

Vanberg

et al. 2021

Monthly Notices of the Royal Astronomical Society

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“…(5) To achieve the optimal performance of DPRWR, the suitable range of FWHM is 1.46 to 4.7 pixels, of bit depth is 4-bit to 16-bit, and of reconstructed mode number is 3 to 6 times the number of sub-apertures. (6) The training set images with weak noise and without subtraction of the global threshold can effectively improve the generalization ability of the neural network under different levels of noise. In this experiment, the wavefront reconstruction performance of the DPRWR based on this scheme is only slightly degraded by 3.5% when dealing with strong noise at a PSNR of 5 compared to the case without noise interference.…”

Section: ⅴ Conclusion and Discussionmentioning

confidence: 99%

“…C21K002). (Corresponding authors: Youming Guo) diversity convolutional neural network, it still focuses on low-order aberrations measurement [6].…”

Section: Introductionmentioning

confidence: 99%

Performance Characterization of Deep-Phase-Retrieval Shack-Hartmann Wavefront Sensors

Zhang

Guo

2023

IEEE Photonics J.

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Shack-Hartmann wavefront sensor (SHWFS) is the most popular wavefront sensor in adaptive optics systems. The Deep-Phase-Retrieval Wavefront Reconstruction (DPRWR) method, which was proposed by our group previously, is a kind of deep learning-based wavefront reconstruction method. It can extract more information from the SHWFS images to accurately obtain more Zernike mode coefficients. However, the application limits, performance upper bound, and noise immunity have not been investigated in detail in previous reports. In this paper, subaperture spot sampling, bit depth, number of reconstructed mode coefficients, and noise intensities are analyzed by simulations and experiments to investigate the influence of changes in these parameters on the performance of DPRWR. This work aims to optimize the configuration of DPRWR for better measurement accuracy, spatial resolution, and robustness.

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“…This class of methods mainly includes iterative-transform methods (developed from the Gerchberg-Saxton algorithm) [ 3 , 4 , 5 , 6 , 7 ], parametric methods (also known as model-based optimization algorithms or directly called phase diversity algorithms) [ 8 , 9 , 10 , 11 , 12 ], and deep learning methods [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Compared to other WFS methods, such as the Shack–Hartmann sensor [ 21 ], pyramid sensor [ 22 ], or curvature sensing [ 23 ], this class of WFS methods is particularly suitable for space applications [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Jitter-Robust Phase Retrieval Wavefront Sensing Algorithms

Guo

et al. 2022

Sensors

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Phase retrieval wavefront sensing methods are now of importance for imaging quality maintenance of space telescopes. However, their accuracy is susceptible to line-of-sight jitter due to the micro-vibration of the platform, which changes the intensity distribution of the image. The effect of the jitter shows some stochastic properties and it is hard to present an analytic solution to this problem. This paper establishes a framework for jitter-robust image-based wavefront sensing algorithm, which utilizes two-dimensional Gaussian convolution to describe the effect of jitter on an image. On this basis, two classes of jitter-robust phase retrieval algorithms are proposed, which can be categorized into iterative-transform algorithms and parametric algorithms, respectively. Further discussions are presented for the cases where the magnitude of jitter is unknown to us. Detailed simulations and a real experiment are performed to demonstrate the effectiveness and practicality of the proposed approaches. This work improves the accuracy and practicality of the phase retrieval wavefront sensing methods in the space condition with non-ignorable micro-vibration.

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Sub-Millisecond Phase Retrieval for Phase-Diversity Wavefront Sensor

Cited by 28 publications

References 30 publications

Focal plane wavefront sensing using machine learning: performance of convolutional neural networks compared to fundamental limits

Focal plane wavefront sensing using machine learning: performance of convolutional neural networks compared to fundamental limits

Performance Characterization of Deep-Phase-Retrieval Shack-Hartmann Wavefront Sensors

Jitter-Robust Phase Retrieval Wavefront Sensing Algorithms

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