@MastersThesis{Monego:2021:ReImAs,
author = "Monego, Vinicius Schmidt",
title = "Restaura{\c{c}}{\~a}o de imagens astron{\^o}micas utilizando
redes neurais, wavelets e regulariza{\c{c}}{\~a}o",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "2021",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2021-03-04",
keywords = "problemas inversos, restaura{\c{c}}{\~a}o de imagens,
ru{\'{\i}}do, regulariza{\c{c}}{\~a}o, transformada wavelet,
inverse problems, image restoration, noise, regularization,
wavelet transform.",
abstract = "Neste trabalho s{\~a}o estudadas diferentes t{\'e}cnicas de
restaura{\c{c}}{\~a}o de imagens, envolvendo m{\'e}todos de
regulariza{\c{c}}{\~a}o, filtros wavelets e redes neurais. Mais
especificamente, as t{\'e}cnicas escolhidas foram
regulariza{\c{c}}{\~a}o por Tikhonov, regulariza{\c{c}}{\~a}o
por entropia, regulariza{\c{c}}{\~a}o da varia{\c{c}}{\~a}o
total, filtro de Wiener, filtragem wavelet, redes neurais
convolucionais e filtro neural multiescala. As imagens de
interesse s{\~a}o imagens astron{\^o}micas, provenientes da
fonte HubbleSite, que disponibiliza imagens do telesc{\'o}pio
espacial Hubble sob uma licen{\c{c}}a compat{\'{\i}}vel a
dom{\'{\i}}nio p{\'u}blico. As imagens s{\~a}o degradadas com
ru{\'{\i}}do gaussiano de desvio padr{\~a}o de 5%, 15% e 25%. A
performance de cada um dos m{\'e}todos de restaura{\c{c}}{\~a}o
{\'e} avaliada atrav{\'e}s das m{\'e}tricas: NRMSE (Normalized
Root-Mean-Square Error Erro M{\'e}dio Quadr{\'a}tico
Normalizado), PSNR (Peak Signal-to-Noise Ratio Raz{\~a}o de pico
sinal-ru{\'{\i}}do) e SSIM (Structural Similarity Index Measure
Medida do {\'{\i}}ndice de similaridade estrutural). ABSTRACT:
In this work, different image restoration techniques are studied,
involving regularization methods, wavelet filters, and neural
networks. More specifically, the techniques chosen were
regularization by Tikhonov, regularization by entropy, total
variation regularization, Wiener filter, wavelet filtering,
convolutional neural networks and multiscale neural filter. The
images of interest are astronomical images from the HubbleSite
source, which makes images from the Hubble space telescope
available under a license compatible with the public domain. The
images are degraded with standard deviation Gaussian noise of 5%,
10% and 15%. The performance of each of the restoration method is
evaluated using the following metrics: Normalized Root-
Mean-Squared Error (NRMSE), Peak Signal-to-Noise Ratio (PSNR) and
Structural Similarity Index Measure (SSIM).",
committee = "Stephany, Stephan and Campos Velho, Haroldo Fraga de (orientador)
and Kozakevicius, Alice de Jesus (orientadora) and Queiroz,
Gilberto Ribeiro de and Silva Neto, Ant{\^o}nio Jos{\'e} da and
Shiguemori, Ana Paula Abrantes Castro",
englishtitle = "Astronomical image restoration using neural networks, wavelets and
regularization",
language = "pt",
pages = "101",
ibi = "8JMKD3MGP3W34R/44ARSBE",
url = "http://urlib.net/ibi/8JMKD3MGP3W34R/44ARSBE",
targetfile = "publicacao.pdf",
urlaccessdate = "09 maio 2024"
}