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BAYESIAN IMAGE SUPERRESOLUTION AND HIDDEN VARIABLE MODELING
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作者 Atsunori KANEMURA Shin-ichi MAEDA +1 位作者 wataru fukuda ShinI SHII 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2010年第1期116-136,共21页
Superresolution is an image processing technique that estimates an original high-resolutionimage from its low-resolution and degraded observations.In superresolution tasks,there have beenproblems regarding the computa... Superresolution is an image processing technique that estimates an original high-resolutionimage from its low-resolution and degraded observations.In superresolution tasks,there have beenproblems regarding the computational cost for the estimation of high-dimensional variables.Theseproblems are now being overcome by the recent development of fast computers and the developmentof powerful computational techniques such as variational Bayesian approximation.This paper reviewsa Bayesian treatment of the superresolution problem and presents its extensions based on hierarchicalmodeling by employing hidden variables. 展开更多
关键词 Bayesian estimation hidden variables image superresolution Markov random fields variational estimation.
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