@InProceedings{HomemMartMasc:2007:SuImRe,
author = "Homem, Murillo Rodrigo Petrucelli and Martins, Ana Lu{\'{\i}}sa
Dine and Mascarenhas, Nelson Delfino d'{\'A}vila",
affiliation = "Departamento de Computa{\c{c}}{\~a}o, Universidade Federal de
S{\~a}o Carlos and Departamento de Computa{\c{c}}{\~a}o,
Universidade Federal de S{\~a}o Carlos and Departamento de
Computa{\c{c}}{\~a}o, Universidade Federal de S{\~a}o Carlos",
title = "Super-Resolution Image Reconstruction using the Discontinuity
Adaptive ICM",
booktitle = "Proceedings...",
year = "2007",
editor = "Gon{\c{c}}alves, Luiz and Wu, Shin Ting",
organization = "Brazilian Symposium on Computer Graphics and Image Processing, 20.
(SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "Super resolution image reconstruction, sub-pixel image
registration.",
abstract = "We propose a Bayesian approach for the super resolution image
reconstruction (SRIR) problem using a Markov random field (MRF)
for image characterization. SRIR consists in using a set of
low-resolution (LR) images from the same scene to generate a
high-resolution (HR) estimate of the original object. Using a
Bayesian formulation, it is possible to incorporate previously
known spatial information about the HR image to be estimated. In
our approach, the iterated conditional modes (ICM) algorithm is
used to find the maximum a posteriori (MAP) solution, and a
discontinuity adaptive framework is used to overcome the
oversmoothness inherent to MAP-MRF formulations. To evaluate the
capability of the algorithm in reconstructing the actual image, we
used the universal image quality index (UIQI). According to this
index, the proposed method produced accurate results.",
conference-location = "Belo Horizonte, MG, Brazil",
conference-year = "7-10 Oct. 2007",
language = "en",
ibi = "6qtX3pFwXQZG2LgkFdY/RvDUS",
url = "http://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/RvDUS",
targetfile = "icm-mrph.pdf",
urlaccessdate = "2024, May 03"
}