Image Restoration with a Truncated Gaussian Model(General)
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概要
- 論文の詳細を見る
The Gaussian model for image restoration has the problem of positive probability densities for pixels outside the realistic range. To solve this problem, we introduce a truncated Gaussian model (TG model). In this model, the tails of the Gaussian distribution are cut off at upper and lower bounds and are replaced by δ peaks at the cut boundaries. We analytically obtain the average performance of the TG model in a mean-field system by solving exactly the infinite-range model and using the replica method. We also compare the infinite-range model to the more realistic two-dimensional case by Monte Carlo simulations. When modeling the TG model, we introduce a generalized prior probability. This prior probability includes the Gaussian, Ising, Q-Ising spin, and TG model as special cases. Thus, we can choose an appropriate model depending on the statistical properties of the images.
- 社団法人日本物理学会の論文
- 2008-03-15
著者
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Tanaka Hiroyuki
Graduate School of Pharmaceutical Sciences, Kyushu University
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Miura Keiji
Graduate School Of Frontier Sciences University Of Tokyo
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OKADA MASATO
Graduate School of Frontier Sciences, The University of Tokyo
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TANAKA Hiroyuki
Toshiba Corporation
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OKADA Masato
University of Tokyo
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Tanaka Hiroyuki
Graduate School Of Pharmaceutical Sciences Kyushu University
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Okada Masato
Division Of Transdisciplinary Of Sciences Graduate School Of Frontier Sciences The University Of Tok
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Okada Masato
Graduate School Of Engineering Science Osaka University
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OKADA Masato
Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute:"Intelligent Cooperation and Control", PRESTO, JST, co RIKEN BSI:Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, the University of To
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