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Astronomical image reconstruction with convolutional neural networks
[article]
2017
arXiv
pre-print
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We investigate in this work the use of convolutional neural networks for image reconstruction in astronomy. ...
State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem. ...
CONVOLUTIONAL NEURAL NETWORK FOR IMAGE RECONSTRUCTION
A. ...
arXiv:1612.04526v2
fatcat:o6c65ujpnjhkrgtracxbzexq24
Astronomical Image Reconstruction With Convolutional Neural Networks
2018
Zenodo
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CONVOLUTIONAL NEURAL NETWORK FOR IMAGE RECONSTRUCTION
A. ...
Again we see that the 3-layer convolutional neural network leads to a more detailed reconstruction.
C. ...
doi:10.5281/zenodo.1159216
fatcat:pueesbuwyzgqdouk6f3h6driie
Predicting galaxy spectra from images with hybrid convolutional neural networks
[article]
2020
arXiv
pre-print
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We present a powerful new approach using a hybrid convolutional neural network with deconvolution instead of batch normalization; this hybrid CNN outperforms other models in our tests. ...
We are able to robustly predict and reconstruct galaxy spectra directly from broad-band imaging. ...
the head of our neural networks. ...
arXiv:2009.12318v1
fatcat:i4ephedeirgahcirrectrta23q
Recognizing three-dimensional phase images with deep learning
[article]
2021
arXiv
pre-print
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Optical phase contains key information for biomedical and astronomical imaging. ...
To address this challenge, we developed a speckle three-dimensional reconstruction network (STRN) to recognize phase objects behind scattering media, which circumvents the limitations of memory effect. ...
The generator is an encode-decode structured neural network that generates reconstructed phase images from the input speckle images. ...
arXiv:2107.10584v1
fatcat:zwbzte677rcqnjl3v5rtmd4fqy
Learning to Denoise Astronomical Images with U-nets
[article]
2020
arXiv
pre-print
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We propose Astro U-net, a convolutional neural network for image de-noising and enhancement. ...
Our network is able to produce images with noise characteristics as if they are obtained with twice the exposure time, and with minimum bias or information loss. ...
METHOD To denoise and enhance the astronomical images, we use convolutional neural networks. ...
arXiv:2011.07002v1
fatcat:d4yv7xt2xzaoxdhi7qoouk5f7q
Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations
2019
Sensors
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These corrections are performed classically with reconstruction algorithms; between them, neural networks showed good results. ...
A reconstruction algorithm is presented, "Shack-Hartmann reconstruction with deep learning on solar–prototype" (proto-HELIOS), to correct on fixed solar images, achieving an average 85.39% of precision ...
At present, the tomographic reconstruction approach with convolutional neural networks is an open line of investigation. ...
doi:10.3390/s19102233
fatcat:smndqbyx6nbknbqkvpz3xaer34
Deep learning wavefront sensing and aberration correction in atmospheric turbulence
2021
PhotoniX
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AbstractDeep 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). ...
The authors thank the Lijiang Astronomical Observatory of the Chinese Academy of Sciences for providing an experimental site. ...
doi:10.1186/s43074-021-00030-4
fatcat:swzlp43d65b57nx7j5gt5pdp2y
Real-time regression analysis with deep convolutional neural networks
[article]
2018
arXiv
pre-print
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We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains. ...
In the context of image classification, we have applied deep learning for the classification of noise anomalies with spectrogram images as inputs to convolutional neural networks (CNNs) [17] , [15] , ...
techniques to increase the sensitivity of neural nets to extract low signal-to-noise ratio signals from noisy time-series; iii) systematic exploration to elucidate why deep convolutional neural networks ...
arXiv:1805.02716v1
fatcat:sp5mghuiy5d5fd5ecbbjl3x2ay
Correlated Read Noise Reduction in Infrared Arrays Using Deep Learning
2022
Astronomical Journal
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We train a convolutional recurrent neural network on simulated astrophysical scenes added to laboratory darks to estimate the flux on each pixel of science images. ...
We present a new procedure rooted in deep learning to construct science images from data cubes collected by astronomical instruments using HxRG detectors in low-flux regimes. ...
Despite the presence of systematic biases, the fact that the neural network can reconstruct this image with acceptable accuracy despite not having been trained using data resembling this image highlights ...
doi:10.3847/1538-3881/ac69d2
fatcat:34fjja7kazdkxaitfibumwbhdq
Blind Image Deconvolution By Neural Recursive Function Approximation
2010
Zenodo
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Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. ...
This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an ...
X Minimization of ( )
E θ with respect to θ is the
goal of supervised learning of RBF neural networks. ...
doi:10.5281/zenodo.1333964
fatcat:5vzlnbyjlrawxpbzikzu4h2rtq
2020 Index IEEE Transactions on Computational Imaging Vol. 6
2020
IEEE Transactions on Computational Imaging
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., +, TCI 2020 1017-1032
Convolutional neural networks
CNF+CT: Context Network Fusion of Cascade-Trained Convolutional Neu-
ral Networks for Image Super-Resolution. ...
Coherent Plug-and-Play: Digital Holographic Imaging Through Atmospheric Turbulence Using Model-Based Iterative Reconstruction and Convolutional Neural Networks. ...
doi:10.1109/tci.2021.3054596
fatcat:puij7ztll5ai7alxrmqzsupcny
Experience with Artificial Neural Networks Applied in Multi-object Adaptive Optics
2019
Publications of the Astronomical Society of the Pacific
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The use of artificial neural networks has evolved to the extent of the creation of a reconstruction technique that is capable of estimating the wavefront of light after being deformed by the atmosphere ...
These techniques have landed in the field of adaptive optics, and are being used to correct distortions caused by atmospheric turbulence in astronomical images obtained by ground-based telescopes. ...
Figure 5 . 5 Example of the topology of a convolutional neural network. ...
doi:10.1088/1538-3873/ab1ebb
fatcat:n67o5dy32vdq3jbo5vjbq2nxi4
The analysis of VERITAS muon images using convolutional neural networks
2016
Proceedings of the International Astronomical Union
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We present preliminary results of a precise classification of a small set of muon events using a convolutional neural networks model with the raw images as input features. ...
We also show the possibility of using the convolutional neural networks model for regression problems, such as the radius and brightness measurement of muon events, which can be used to calibrate the throughput ...
fully-connected neural network classification model after the convolutional layers. ...
doi:10.1017/s1743921316012734
fatcat:xaurhwxowvfevbkzwn4jy52h6m
GammaLearn - first steps to apply Deep Learning to the Cherenkov Telescope Array data
2019
EPJ Web of Conferences
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Moreover, the trained neural networks show very good computing performances during execution. ...
Due to its very high sensitivity, CTA will record a colossal amount of data that represent a computing challenge to the reconstruction software. ...
A common approach, allowing to use traditional image processing frameworks and in particular convolutional neural network ones, is to re-sample these unconventional images into ones with a rectangular ...
doi:10.1051/epjconf/201921406020
fatcat:gr4zoz7gazfzdfignauazc6eg4
Adaptive optics based on machine learning: a review
2022
Opto-Electronic Advances
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In recent years, with the rapid development of artificial intelligence, adaptive optics will be boosted dramatically. ...
presented a method using convolutional neural network (CNN) as a reconstruction alternative in MOAO 74 . ...
With suitable computer hardware, it is anticipated that on-site real-time reconstruction of astronomical images will be possible in the near future. ...
doi:10.29026/oea.2022.200082
fatcat:fkuhbsrwpvcxnca35tz54hx2lm
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