Wavefront reconstruction and prediction with convolutional neural networks

Swanson, Robin; Lamb, Masen; Correia, Carlos; Sivanandam, Suresh; Kutulakos, Kiriakos N.

doi:10.1117/12.2312590

Cited by 35 publications

(26 citation statements)

References 3 publications

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“…For AO wavefront reconstruction networks, many recent works that either to infer Zernike coe±cients for wavefront correction 73 or to map SH slopes to true wavefront data, 67 have adopted the CNN architecture, for its advantage to extract image features. And there is nothing obvious about these networks.…”

Section: Discussionmentioning

confidence: 99%

“…Apart from more complex AO retinal imaging process for further modeling and training with the network, more e®ective regularization e®ects for the deep neural network such as dropout and batch normalization can improve the network's performance in deblurring the AO retinal images. There are also many examples 67,110,111 where popular networks from the¯eld of computer vision are employed to train and exploit data for wavefront reconstruction, image enhancement and restoration.…”

Section: Discussionmentioning

confidence: 99%

“…Such implementations, however, are inherently limited by the number of lenslets in the system and prone to certain error modes. 67 The concepts of AI, especially ANN, have not been used for AO for long time, [68][69][70] but this area has recently revived, bringing new hopes to overcome the aforementioned limitations. For example, in 2014 Osborn et al 71 reported a single hidden layer multilayer perceptron to infer the Zernike polynomials from the SH slopes for wavefront reconstruction; in 2018 Swanson et al 67 employed a U-Net architecture to learn a mapping from the SH slopes to the true wavefront slope data, based on which the latent atmospheric wavefront was reconstructed and a convolutional LSTM network was employed to predict future wavefronts with only¯ve previous data samples; also in 2018, Suarez et al 72 proposed a CNN-based reconstructor relying on the full measurement images of the SH wavefront sensor; in 2019 Ma et al 73 used a CNN to extract features from the two intensity images of wavefront distortions and obtained the corresponding Zernike coe±cients, based on which satisfactory AO compensation e®ect was achieved.…”

Section: Integration Of Ai With Aomentioning

confidence: 99%

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

Cheng

Luo

et al. 2019

J. Innov. Opt. Health Sci.

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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 scattering, featured by random interference, namely speckle patterns. This randomness occurs due to the refractive index inhomogeneity in complex media like biological tissue or the modal dispersion in multimode fiber, yet this randomness is actually deterministic and potentially can be time reversal or precompensated. Various wavefront shaping approaches, such as optical phase conjugation, iterative optimization, and transmission matrix measurement, have been developed to generate tight and intense optical delivery or high-resolution image of an optical object behind or within a scattering medium. The performance of these modulations, however, is far from satisfaction. Most recently, artificial intelligence has brought new inspirations to this field, providing exciting hopes to tackle the challenges by mapping the input and output optical patterns and building a neuron network that inherently links them. In this paper, we survey the developments to date on this topic and briefly discuss our views on how to harness machine learning (deep learning in particular) for further advancements in the field.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Integration Of Ai With Aomentioning

confidence: 99%

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

Cheng

Luo

et al. 2019

J. Innov. Opt. Health Sci.

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show abstract

“…Machine learning offers novel approaches to correct for aberrations encountered when imaging though scattering materials, [1][2][3][4] from astronomy [5][6][7][8][9] to microscopy with transmitted (for example [10][11][12]) and reflected light [13]. To find aberration corrections in these situations, machine learning typically relies on large synthetic datasets.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, training datasets are often based on combinations of Zernike polynomials [5][6][7][8][9][10][11][12][13] which might however not accurately capture all aspects of experimentally encountered aberrations. Additionally, for more strongly scattering samples, which require increasingly higher orders of Zernike modes, covering all potential scattering situations by sampling a sufficient number of different mode combinations eventually results in very large datasets.…”

Section: Introductionmentioning

confidence: 99%

Differentiable model-based adaptive optics with transmitted and reflected light

Vishniakou¹,

Seelig²

2020

Opt. Express

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Aberrations limit optical systems in many situations, for example when imaging in biological tissue. Machine learning offers novel ways to improve imaging under such conditions by learning inverse models of aberrations. Learning requires datasets that cover a wide range of possible aberrations, which however becomes limiting for more strongly scattering samples, and does not take advantage of prior information about the imaging process. Here, we show that combining model-based adaptive optics with the optimization techniques of machine learning frameworks can find aberration corrections with a small number of measurements. Corrections are determined in a transmission configuration through a single aberrating layer and in a reflection configuration through two different layers at the same time. Additionally, corrections are not limited by a predetermined model of aberrations (such as combinations of Zernike modes). Focusing in transmission can be achieved based only on reflected light, compatible with an epidetection imaging configuration.

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High-precision wavefront reconstruction from Shack-Hartmann wavefront sensor data by a deep convolutional neural network

Gu¹,

Zhao²,

Zhang³

et al. 2021

Meas. Sci. Technol.

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The Shack–Hartmann wavefront sensor (SHWFS) has been widely used for measuring aberrations in adaptive optics systems. However, its traditional wavefront reconstruction method usually has limited precision under field conditions because the weight-of-center calculation is affected by many factors, such as low signal-to-noise-ratio objects, strong turbulence, and so on. In this paper, we present a ResNet50+ network that reconstructs the wavefront with high precision from the spot pattern of the SHWFS. In this method, a nonlinear relationship is built between the spot pattern and the corresponding Zernike coefficients without using a traditional weight-of-center calculation. The results indicate that the root-mean-square (RMS) value of the residual wavefront is 0.0128 μm, which is 0.79% of the original wavefront RMS. Additionally, we can reconstruct the wavefront under atmospheric conditions, if the ratio between the telescope aperture’s diameter D and the coherent length r 0 is 20 or if a natural guide star of the ninth magnitude is available, with an RMS reconstruction error of less than 0.1 μm. The method presented is effective in the measurement of wavefronts disturbed by atmospheric turbulence for the observation of weak astronomical objects.

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Wavefront reconstruction and prediction with convolutional neural networks

Cited by 35 publications

References 3 publications

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

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

Differentiable model-based adaptive optics with transmitted and reflected light

High-precision wavefront reconstruction from Shack-Hartmann wavefront sensor data by a deep convolutional neural network

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