Q-伊辛模型的贝叶斯最优逆半调和统计力学
Bayes-optimal inverse halftoning and statistical mechanics of the Q-Ising model
摘要：
基于Q-伊辛模型的统计力学，对图像的逆半调（用黑白点表示灰度的逆过程）问题，给出了贝叶斯推断。利用蒙特卡罗模拟，我们研究了逆过程的统计特性，特别是，我们揭示了贝叶斯最优解的条件，使其均方误差取最小值。数值结果定性地证实了无限程模型的分析。作为我们的方法的演示，我们应用该方法来检索灰度图像，如标准图像“Lenna”，从半色调版本。我们发现，贝叶斯最优解给出了一个很好的恢复灰度图像，这是非常接近原来的。
英文摘要：
On the basis of statistical mechanics of the Q-Ising model, we formulate the Bayesian inference to the problem of inverse halftoning, which is the inverse process of representing gray-scales in images by means of black and white dots. Using Monte Carlo simulations, we investigate statistical properties of the inverse process, especially, we reveal the condition of the Bayes-optimal solution for which the mean-square error takes its minimum. The numerical result is qualitatively confirmed by analysis of the infinite-range model. As demonstrations of our approach, we apply the method to retrieve a grayscale image, such as standard image `Lenna', from the halftoned version. We find that the Bayes-optimal solution gives a fine restored grayscale image which is very close to the original.
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