Wide field astronomical image restoration with convolutional neural networks

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dc.catalogadordfo
dc.contributor.advisorTorres Torriti, Miguel Attilio
dc.contributor.advisorGuzmán Carmine, Christian Dani
dc.contributor.authorBernardi, Rafael Luiz
dc.contributor.otherPontificia Universidad Católica de Chile. Escuela de Ingeniería
dc.date.accessioned2023-11-17T15:50:07Z
dc.date.available2023-11-17T15:50:07Z
dc.date.issued2023
dc.description.abstractMost image restoration methods in astronomy rely upon probabilistic tools that infer a best solution for a deconvolution problem. They achieve good performances when the Point Spread Function (PSF) is spatially invariable in the image plane. However, this later condition is not always satisfied for real optical systems. PSF angular variations cannot be evaluated directly from the observations, neither be corrected at a pixel resolution. The new method for the restoration of images affected by static and anisotropic aberrations is developed using Deep Neural Networks that can be directly applied to sky images. The network is trained using simulated sky images corresponding to the T80-S Telescope optical model, an 80 cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system and real observation conditions, like atmospheric turbulence and detector noises. Once trained, the network is used directly on real images from the T80-S telescope, resulting in a prediction that is a corrected version of the image, characterized by a known and constant PSF across the field of view, zero noise and compensation for the effect of atmospheric turbulence. The method tested on real T80-S images was compared to the relative positions of objects in the GAIA survey catalog.
dc.fechaingreso.objetodigital2023-11-17
dc.format.extentxxi, 349 páginas
dc.fuente.origenSRIA
dc.identifier.doi10.7764/tesisUC/ING/75327
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75327
dc.identifier.urihttps://doi.org/10.7764/tesisUC/ING/75327
dc.information.autorucEscuela de Ingeniería ; Torres Torriti, Miguel Attilio ; 0000-0002-7904-7981 ; 96590
dc.information.autorucEscuela de Ingeniería ; Bernardi, Rafael Luiz ; 0000-0002-3255-8610 ; 1050889
dc.information.autorucEscuela de Ingeniería ; Guzmán Carmine, Christian Dani ; S/I ; 93452
dc.language.isoen
dc.nota.accesoContenido completo
dc.rightsacceso abierto
dc.subjectAstronomy
dc.subjectImage restoration
dc.subjectConvolutional neural networks
dc.subjectArtificial intelligence
dc.subjectSpatially variable point spread function
dc.subject.ddc520
dc.subject.deweyAstronomíaes_ES
dc.titleWide field astronomical image restoration with convolutional neural networks
dc.typetesis doctoral
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