Numerical study of adaptive optics compensation based on Convolutional Neural Networks

2019 ◽  
Vol 433 ◽  
pp. 283-289 ◽  
Author(s):  
Huimin Ma ◽  
Haiqiu Liu ◽  
Yan Qiao ◽  
Xiaohong Li ◽  
Wu Zhang
Keyword(s):  
Neural Networks ◽  
Adaptive Optics ◽  
Convolutional Neural Networks ◽  
Numerical Study

scholarly journals Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6686
Author(s):  
Bartosz Miller ◽  
Leonard Ziemiański
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Composite Structures ◽  
Vibration Mode ◽  
Numerical Study ◽  
Mode Shapes ◽  
Zone Size ◽  
Composite Cylinder ◽  
Material Degradation ◽  
Damaged Zone

This paper presents a numerical study of the feasibility of using vibration mode shapes to identify material degradation in composite structures. The considered structure is a multilayer composite cylinder, while the material degradation zone is, for simplicity, considered a square section of the lateral surface of the cylinder. The material degradation zone size and location along the cylinder axis are identified using a deep learning approach (convolutional neural networks, CNNs, are applied) on the basis of previously identified vibration mode shapes. The different numbers and combinations of identified mode shapes used to assess the damaged zone size and location were analyzed in detail. The final selection of mode shapes considered in the identification procedure yielded high accuracy in the identification of the degradation zone.

New adaptive optics Tomographic Pupil Image reconstructor based on convolutional neural networks

2017 ◽  
Author(s):  
Carlos González-Gutiérrez ◽  
Juan José Férnández Valdivia ◽  
Sergio Luis Suárez Gómez ◽  
José Manuel Rodríguez Ramos ◽  
Luis Fernando Rodríguez Ramos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Adaptive Optics ◽  
Convolutional Neural Networks

scholarly journals Deblurring adaptive optics retinal images using deep convolutional neural networks

2017 ◽  
Vol 8 (12) ◽  
pp. 5675 ◽  
Author(s):  
Xiao Fei ◽  
Junlei Zhao ◽  
Haoxin Zhao ◽  
Dai Yun ◽  
Yudong Zhang
Keyword(s):  
Neural Networks ◽  
Adaptive Optics ◽  
Convolutional Neural Networks ◽  
Retinal Images ◽  
Deep Convolutional Neural Networks

scholarly journals Overview and Choice of Artificial Intelligence Approaches for Night-Time Adaptive Optics Reconstruction

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1220
Author(s):  
Francisco García Riesgo ◽  
Sergio Luis Suárez Gómez ◽  
Jesús Daniel Santos ◽  
Enrique Díez Alonso ◽  
Fernando Sánchez Lasheras
Keyword(s):  
Neural Networks ◽  
Adaptive Optics ◽  
Convolutional Neural Networks ◽  
Least Squares Method ◽  
Computational Time ◽  
Image Correction ◽  
Night Time ◽  
Model Complex ◽  
Systems Requirements ◽  
Under Construction

Adaptive optics (AO) is one of the most relevant systems for ground-based telescopes image correction. AO is characterized by demanding computational systems that must be able to quickly manage large amounts of data, trying to make all the calculations needed the closest to real-time. Furthermore, next generations of telescopes that are already being constructed will demand higher computational requirements. For these reasons, artificial neural networks (ANNs) have recently become one alternative to commonly used tomographic reconstructions based on several algorithms as the least-squares method. ANNs have shown its capacity to model complex physical systems, as well as predicting values in the case of nocturnal AO where some models have already been tested. In this research, a comparison in terms of quality of the outputs given and computational time needed is presented between three of the most common ANN topologies used nowadays, to obtain the one that fits better these AO systems requirements. Multi-layer perceptron (MLP), convolutional neural networks (CNN) and fully convolutional neural networks (FCN) are considered. The results presented determine the way forward for the development of reconstruction systems based on ANNs for future telescopes, as the ones being under construction for solar observations.

scholarly journals Defocused Image Deep Learning Designed for Wavefront Reconstruction in Tomographic Pupil Image Sensors

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 15
Author(s):  
Sergio Luis Suárez Gómez ◽  
Francisco García Riesgo ◽  
Carlos González Gutiérrez ◽  
Luis Fernando Rodríguez Ramos ◽  
Jesús Daniel Santos
Keyword(s):  
Neural Networks ◽  
Adaptive Optics ◽  
Convolutional Neural Networks ◽  
Image Sensors ◽  
Wavefront Reconstruction ◽  
Least Squares Fit ◽  
Astronomical Images ◽  
Defocused Image ◽  
Zernike Modes ◽  
Modelling Methods

Mathematical modelling methods have several limitations when addressing complex physics whose calculations require considerable amount of time. This is the case of adaptive optics, a series of techniques used to process and improve the resolution of astronomical images acquired from ground-based telescopes due to the aberrations introduced by the atmosphere. Usually, with adaptive optics the wavefront is measured with sensors and then reconstructed and corrected by means of a deformable mirror. An improvement in the reconstruction of the wavefront is presented in this work, using convolutional neural networks (CNN) for data obtained from the Tomographic Pupil Image Wavefront Sensor (TPI-WFS). The TPI-WFS is a modified curvature sensor, designed for measuring atmospheric turbulences with defocused wavefront images. CNNs are well-known techniques for its capacity to model and predict complex systems. The results obtained from the presented reconstructor, named Convolutional Neural Networks in Defocused Pupil Images (CRONOS), are compared with the results of Wave-Front Reconstruction (WFR) software, initially developed for the TPI-WFS measurements, based on the least-squares fit. The performance of both reconstruction techniques is tested for 153 Zernike modes and with simulated noise. In general, CRONOS showed better performance than the reconstruction from WFR in most of the turbulent profiles, with significant improvements found for the most turbulent profiles; overall, obtaining around 7% of improvements in wavefront restoration, and 18% of improvements in Strehl.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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