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Bayesian image superresolution and hidden variable modeling

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Abstract

Superresolution is an image processing technique that estimates an original high-resolution image from its low-resolution and degraded observations. In superresolution tasks, there have been problems regarding the computational cost for the estimation of high-dimensional variables. These problems are now being overcome by the recent development of fast computers and the development of powerful computational techniques such as variational Bayesian approximation. This paper reviews a Bayesian treatment of the superresolution problem and presents its extensions based on hierarchical modeling by employing hidden variables.

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Authors and Affiliations

  1. Graduate School of Informatics, Kyoto University, Kyoto, 611-0011, Japan

    Atsunori Kanemura, Shin-ichi Maeda, Wataru Fukuda & Shin Ishii

Authors
  1. Atsunori Kanemura
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  2. Shin-ichi Maeda
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  3. Wataru Fukuda
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  4. Shin Ishii
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Corresponding author

Correspondence to Atsunori Kanemura.

Additional information

This type of superresolution is in particular called multiframe superresolution or reconstruction-based superresolution. There is another type of superresolution called example-based superresolution[5], which estimates a high-resolution image from only one image rather than from multiple images, based on a database developed in advance. Example-based methods constitute another large class but they are beyond the scope of this article.

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Kanemura, A., Maeda, Si., Fukuda, W. et al. Bayesian image superresolution and hidden variable modeling. J Syst Sci Complex 23, 116–136 (2010). https://doi.org/10.1007/s11424-010-9277-0

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  • DOI: https://doi.org/10.1007/s11424-010-9277-0

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