Naive mean field approximation for image restoration
Naive mean field approximation for image restoration
复制标题
用于图像恢复的朴素平均场近似
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
M. Okada
中科院分区：
文献类型：
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作者：
Hayaru Shouno;Koji Wada;M. Okada
我们尝试在贝叶斯推理的框架下进行图像恢复。最近的研究表明，在一定的准则下，与能量最小化相对应的最大后验概率(MAP)估计可以被相当于有限温度译码方法的MPM(后验边缘极大值)估计所超越。由于MPM估计需要大量的计算时间来计算热平均值，因此通常采用确定算法平均场方法来避免这一困难。在图像复原的框架下，我们提出了一种朴素平均场近似的统计力学分析。我们将我们的理论结果与计算机模拟的结果进行了比较，并研究了朴素平均场近似的可能性。
We attempt image restoration in the framework of the Bayesian inference. Recently, it has been shown that under a certain criterion the MAP (Maximum A Posterior) estimate, which corresponds to the minimization of energy, can be outperformed by the MPM (Maximizer of the Posterior Marginals) estimate, which is equivalent to a finite-temperature decoding method. Since a lot of computational time is needed for the MPM estimate to calculate the thermal averages, the mean field method, which is a deterministic algorithm, is often utilized to avoid this difficulty. We present a statistical-mechanical analysis of naive mean field approximation in the framework of image restoration. We compare our theoretical results with those of computer simulation, and investigate the potential of naive mean field approximation.