Posterior-mean super-resolution with a causal gaussian markov random field prior

Takayuki Katsuki*, Akira Torii, Masato Inoue

*この研究の対応する著者

研究成果: Article査読

24 被引用数 (Scopus)

抄録

We propose a Bayesian image super-resolution (SR) method with a causal Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from given multiple low-resolution images. An MRF model with the line process supplies a preferable prior for natural images with edges. We improve the existing image transformation model, the compound MRF model, and its hyperparameter prior model. We also derive the optimal estimatornot the joint maximum a posteriori (MAP) or the marginalized maximum likelihood (ML) but the posterior mean (PM)from the objective function of the L2-norm-based (mean square error) peak signal-to-noise ratio. Point estimates such as MAP and ML are generally not stable in ill-posed high-dimensional problems because of overfitting, whereas PM is a stable estimator because all the parameters in the model are evaluated as distributions. The estimator is numerically determined by using the variational Bayesian method. The variational Bayesian method is a widely used method that approximately determines a complicated posterior distribution, but it is generally hard to use because it needs the conjugate prior. We solve this problem with simple Taylor approximations. Experimental results have shown that the proposed method is more accurate or comparable to existing methods.

本文言語English
論文番号6161646
ページ(範囲)3182-3193
ページ数12
ジャーナルIEEE Transactions on Image Processing
21
7
DOI
出版ステータスPublished - 2012 7月

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ グラフィックスおよびコンピュータ支援設計

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