Compensating Atmospheric Turbulence with Convolutional Neural Networks for Defocused Pupil Image Wave-Front Sensors

Gómez, Sergio Luis Suárez; González-Gutiérrez, Carlos; Alonso, Enrique Díez; Rodríguez, Jesús Daniel Santos; Bonavera, L.; Valdivia, Juan J. F.; Ramos, Juan Manuel Sánchez; Ramos, Luis Fernando Rodríguez

doi:10.1007/978-3-319-92639-1_34

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…The kernels are applied at certain stride, requiring in some situations to add a padding to the sample. Once the kernels have been applied over all the sample, a feature map is obtained for each kernel in the convolutional layer [46].…”

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations

Gómez

González-Gutiérrez

Riesgo

et al. 2019

Sensors

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Correcting atmospheric turbulence effects in light with Adaptive Optics is necessary, since it produces aberrations in the wavefront of astronomical objects observed with telescopes from Earth. These corrections are performed classically with reconstruction algorithms; between them, neural networks showed good results. In the context of solar observation, the usage of Adaptive Optics on solar differs from nocturnal operations, bringing up a challenge to correct the image aberrations. In this work, a convolutional approach is given to address this issue, considering SCAO configurations. 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 in the reconstruction. Additionally, results encourage to continue working with these techniques to achieve a reconstruction technique for all the regions of the sun.

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Section: Methodsmentioning

confidence: 99%

Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations

Gómez

González-Gutiérrez

Riesgo

et al. 2019

Sensors

Self Cite

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“…Another example of these types of plenoptic cameras is the CAFADIS (acronym for Cámara de Fase-Distancia or Phase-Distance Camera) camera [112], developed by the Universidad de La Laguna in Spain, composed of microlenses placed at a telescope focus and it serves as a wavefront sensor. It can measure distances and estimates optical wavefronts at the same time and current on-going experiments are aimed at telemetry and astronomical observation, such as measurements of the atmospheric turbulence and the height variations of the LGS (laser guided star) beacons [123].…”

Section: Plenoptic Cameras General Descriptionmentioning

confidence: 99%

Towards soft X-ray imaging: results in plasma-based lasers and plenoptic imaging

Gil¹

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Convolutional CARMEN: Tomographic Reconstruction for Night Observation

Riesgo

Gómez

Lasheras

et al. 2019

Lecture Notes in Computer Science

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Compensating Atmospheric Turbulence with Convolutional Neural Networks for Defocused Pupil Image Wave-Front Sensors

Cited by 4 publications

References 21 publications

Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations

Convolutional Neural Networks Approach for Solar Reconstruction in SCAO Configurations

Towards soft X-ray imaging: results in plasma-based lasers and plenoptic imaging

Convolutional CARMEN: Tomographic Reconstruction for Night Observation

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