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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Edge-Preserving Bayesian Image Superresolution Based on Compound Markov Random Fields
verfasst von : Atsunori Kanemura, Shin-ichi Maeda, Shin Ishii
Erschienen in: Artificial Neural Networks – ICANN 2007
Verlag: Springer Berlin Heidelberg
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This study deals with image superresolution problems simultaneously with accompanying image registration problems. The goal of superresolution is to generate a high resolution image by integrating low-resolution degraded observed images. We propose a Bayesian approach whose prior is modeled as a compound Gaussian Markov random field (MRF). This approach is advantageous in preserving discontinuity in the original image, in comparison to the existing single-layer Gaussian MRF models. Maximum-marginalized-likelihood estimation of the registration parameters is carried out by a variational EM algorithm where hidden variables are marginalized out and the posterior distribution is approximated by a factorized trial distribution. High resolution image estimates are obtained as by-products of the EM algorithm. Experiments show that our Bayesian approach with two-layer compound models exhibits better performance in terms of mean square error and visual quality than the single-layer model.