[1]SAIKA Yohei,AOKI Toshizumi.Thermodynamicsinspired inverse halftoning via multiple halftone images[J].智能系统学报,2012,7(01):86-94.
 SAIKA Yohei,AOKI Toshizumi.Thermodynamicsinspired inverse halftoning via multiple halftone images[J].CAAI Transactions on Intelligent Systems,2012,7(01):86-94.
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Thermodynamicsinspired inverse halftoning via multiple halftone images(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年01期
页码:
86-94
栏目:
出版日期:
2012-02-25

文章信息/Info

Title:
Thermodynamicsinspired inverse halftoning via multiple halftone images
文章编号:
1643-4785(2012)01-0086-09
作者:
SAIKA Yohei1 AOKI Toshizumi2
1. Department of Information and Computer Engineering, Gunma National College of Technology, 580 Toriba, Maebashi, 3718530, Japan;
 2. Department of Natural Science, Gunma National College of Technology, 580 Toriba, Maebashi, 3718530, Japan
Author(s):
SAIKA Yohei1 AOKI Toshizumi2
1. Department of Information and Computer Engineering, Gunma National College of Technology, 580 Toriba, Maebashi, 3718530, Japan;
2. Department of Natural Science, Gunma National College of Technology, 580 Toriba, Maebashi, 3718530, Japan
关键词:
inverse halftoning statistical mechanics Monte Carlo simulation Bethe approximation
Keywords:
inverse halftoning statistical mechanics Monte Carlo simulation Bethe approximation
分类号:
TP39
文献标志码:
A
摘要:
Based on an analogy between thermodynamics and Bayesian inference, inverse halftoning was formulated using multiple halftone images based on Bayesian inference using the maximizer of the posterior marginal (MPM) estimate. Applying Monte Carlo simulation to a set of snapshots of the QIsing model, it was demonstrated that optimal performance is achieved around the Bayesoptimal condition within statistical uncertainty and that the performance of the Bayesoptimal solution is superior to that of the maximumaposteriori(MAP) estimation which is a deterministic limit of the MPM estimate. These properties were qualitatively confirmed by the meanfield theory using an infiniterange model established in statistical mechanics. Additionally, a practical and useful method was constructed using the statistical mechanical iterative method via the Bethe approximation. Numerical simulations for a 256grayscale standard image show that Bethe approximation works as good as the MPM estimation if the parameters are set appropriately. 
Abstract:
Based on an analogy between thermodynamics and Bayesian inference, inverse halftoning was formulated using multiple halftone images based on Bayesian inference using the maximizer of the posterior marginal (MPM) estimate. Applying Monte Carlo simulation to a set of snapshots of the QIsing model, it was demonstrated that optimal performance is achieved around the Bayesoptimal condition within statistical uncertainty and that the performance of the Bayesoptimal solution is superior to that of the maximumaposteriori(MAP) estimation which is a deterministic limit of the MPM estimate. These properties were qualitatively confirmed by the meanfield theory using an infiniterange model established in statistical mechanics. Additionally, a practical and useful method was constructed using the statistical mechanical iterative method via the Bethe approximation. Numerical simulations for a 256grayscale standard image show that Bethe approximation works as good as the MPM estimation if the parameters are set appropriately. 

参考文献/References:

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备注/Memo

备注/Memo:
Received Date: 2011-11-01.
 Corresponding Author: SAIKA Yohei.        E-mail:yoheisaika@gmail.com.
About the authors:
SAIKA Yohei,received a B.S. degree in physics from Tokyo Science University, Tokyo, Japan in 1989, an M.S. degree in physics from Tokyo Institute of Technology in 1991, and a Ph.D. degree in physics from Tokyo Institute of Technology in 1995.
AOKI Toshizumi,received a B.S. degree in physics from Nagoya University, Nagoya, Japan in 1976, an M.S. degree in physics from Nagoya University in 1978, and a D.Sc. in physics from Nagoya University in 1981.
更新日期/Last Update: 2012-05-07