Image Segmentation and Restoration Using Switching State-Space Model and Variational Bayesian Method

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概要

• 論文の詳細を見る

• We derive a deterministic algorithm that restores and segments an image using a switching state-space model and a variational Bayesian method. This algorithm estimates hyperparameters as well as infers the original image and latent variables by Bayesian inference. The smoothness of an image is considered to depend on the region. Here, the smoothness indicates what degree each region in the original image is smooth or rough. The novelty of the proposed algorithm is its ability to estimate hyperparameters that control the smoothness of each region of the original image. The hyperparameter that controls noise added in the observation or transmission process is also estimated. Through experiments using artificial images and a natural image degraded by Gaussian noise, we show that the derived algorithm has the potential ability to enable restoration and segmentation from only one noisy image.

• 2012-09-15

著者

• Takiyama Ken

  Graduate School Of Engineering Hiroshima University

• Okada Masato

  Graduate School Of Engineering Science Osaka University

• Miyoshi Seiji

  Faculty of Engineering Science, Kansai University, 3-3-35 Yamate-cho, Suita, Osaka 564-8680

• Takiyama Ken

  Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan

• Hasegawa Ryota

  Graduate School of Science and Engineering, Kansai University, Suita, Osaka 564-8680, Japan

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