Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2007: administrator
Metadata Last Update2020: administrator
Citation KeyHomemMartMasc:2007:SuImRe
TitleSuper-Resolution Image Reconstruction using the Discontinuity Adaptive ICM
DateOct. 7-10, 2007
Access Date2021, Jan. 16
Number of Files1
Size87 KiB
Context area
Author1 Homem, Murillo Rodrigo Petrucelli
2 Martins, Ana Luísa Dine
3 Mascarenhas, Nelson Delfino d'Ávila
Affiliation1 Departamento de Computação, Universidade Federal de São Carlos
2 Departamento de Computação, Universidade Federal de São Carlos
3 Departamento de Computação, Universidade Federal de São Carlos
EditorGonçalves, Luiz
Wu, Shin Ting
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 20 (SIBGRAPI)
Conference LocationBelo Horizonte
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeTechnical Poster
History2008-07-17 14:09:45 :: -> administrator ::
2009-08-13 20:38:41 :: administrator -> banon ::
2010-08-28 20:02:32 :: banon -> administrator ::
2020-02-19 03:06:19 :: administrator -> :: 2007
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsSuper resolution image reconstruction, sub-pixel image registration.
AbstractWe 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.
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