Image Restoration and Segmentation using Region-Based Latent Variables: Bayesian Inference Based on Variational Method
Image Restoration and Segmentation using Region-Based Latent Variables: Bayesian Inference Based on Variational Method
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DOI：
10.1143/jpsj.80.014802
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发表时间：
2011-01
影响因子：
1.7
通讯作者：
S. Miyoshi;M. Okada
中科院分区：
文献类型：
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作者：
S. Miyoshi;M. Okada
在基于贝叶斯推理的图像处理中，为了表示边缘，引入潜变量是非常有效的。在本文中，我们推导出一个确定性的算法，恢复和分割图像，使用基于区域的潜变量和变分推理。该算法估计两个超参数以及推断原始图像和潜变量。此外，该算法通过最小化变分自由能进行模型选择。通过热浴法生成的人工图像和高斯噪声退化的自然图像的实验，证明了该算法的有效性和局限性。
To represent edges in image processing based on Bayesian inference, it is very effective to introduce latent variables. In this paper, we derive a deterministic algorithm that restores and segments an image using region-based latent variables and variational inference. This algorithm estimates two hyperparameters as well as infers the original image and the latent variables. In addition, the algorithm carries out model selection by minimizing the variational free energy. Through experiments using an artificial image generated by the heat bath method and natural images degraded by Gaussian noises, the effectiveness and limitations of the derived algorithm are shown.