PGCC
PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO
PROGRAMAS DE PÓS-GRADUACAO - DC
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E-mail:
ppgcc@ufersa.edu.br
https://ppgcc.ufersa.edu.br/
Banca de DEFESA: NICHOLAS ANDRÉ PINHO DE OLIVEIRA
Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.DISCENTE: NICHOLAS ANDRÉ PINHO DE OLIVEIRA
DATA: 02/12/2018
HORA: 08:30
LOCAL: Auditório CCEN - UFERSA
TÍTULO:
Single image super-resolution method based on linear regression and Box-Cox transformation
PALAVRAS-CHAVES:
1. digital image processing. 2. superresolution. 3. linear regression. 4. box-cox. 5. image zooming.
PÁGINAS: 74
GRANDE ÁREA: Ciências Exatas e da Terra
ÁREA: Ciência da Computação
RESUMO:
Image super-resolution is an essential operation in digital image processing and aims at recovering a high-resolution image from one or more low-resolution input images. The process in which a high-resolution image is obtained involves the creation of new and unknown pixels that must be estimated. Super-resolution algorithms can be classified as multi-image SR and single-image SR. Multi-image SR methods utilize multiple lowresolution
image as input, whereas single-image SR methods utilize only one low-resolution image. Traditional methods such as nearest interpolation, bicubic interpolation and bilinear interpolation are all example of single-image SR methods. These methods, while simple, introduces many artifacts in the high-resolution images. Several methods have emerged in the past decade with the goal of obtaining better, artifacts-free high-resolution images. This thesis proposes a novel single image super-resolution method, that uses a multiple linear regression model and the Box-Cox transform to interpolate the unknown pixels in the high-resolution image. Our proposed method is compared against the classical interpolation methods using the Ultra Eye image data set. The methods are compared by
building confidence intervals using the bootstrap method.
image as input, whereas single-image SR methods utilize only one low-resolution image. Traditional methods such as nearest interpolation, bicubic interpolation and bilinear interpolation are all example of single-image SR methods. These methods, while simple, introduces many artifacts in the high-resolution images. Several methods have emerged in the past decade with the goal of obtaining better, artifacts-free high-resolution images. This thesis proposes a novel single image super-resolution method, that uses a multiple linear regression model and the Box-Cox transform to interpolate the unknown pixels in the high-resolution image. Our proposed method is compared against the classical interpolation methods using the Ultra Eye image data set. The methods are compared by
building confidence intervals using the bootstrap method.
MEMBROS DA BANCA:
Presidente - 1521615 - ANGELICA FELIX DE CASTRO
Externo ao Programa - 1805780 - DANIEL FAUSTINO LACERDA DE SOUZA
Externo ao Programa - 2140339 - LEANDRO CARLOS DE SOUZA
Interno - 2073862 - LEIVA CASEMIRO OLIVEIRA
Externo à Instituição - RENATA MARIA CARDOSO RODRIGUES DE SOUZA - UFPE
Notícia cadastrada em: 27/11/2018 07:21
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