Poisson Observed Image Restoration using a Latent Variational Approximation with Gaussian MRF
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概要
- 論文の詳細を見る
We treat an image restoration problem throughout a Poisson noise channel. The Poisson randomness might be appeared in observation of low contrast object, and its variable takes discrete and positive value. The Poisson noise observation is often hard to treat in a theoretical analysis. In our formulation, we interpret the Poisson noise channel observation as a Bernoulli process, and apply a latent variable method to transform the observation as a Gaussian process with single latent variable. We formulate the image restoration problem as a Bayesian approach, and introduce a Gaussian Markov random field as its prior. The latent parameters and Poisson parameters are treated as hyper-parameters, and we infer them in the expectation maximization framework.
- 一般社団法人情報処理学会の論文
- 2013-07-15
著者
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Hayaru Shouno
Graduate School of Informatics and Engineering, University of Electro-Communications
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Hayaru Shouno
Graduate School Of Informatics And Engineering University Of Electro-communications
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Masato Okada
Graduate School Of Frontier Science The University Of Tokyo|brain Science Institute Riken|japan Scie
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Masato Okada
Graduate School of Frontier Sciences, The University of Tokyo
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Masato Okada
Graduate School of Frontier Science, The University of Tokyo
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