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Microscopic image restoration based on tensor factorization of rotated patches

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Abstract

In microscopic image processing for analyzing biological objects, structural characters of objects such as symmetry and orientation can be used as a prior knowledge to improve the results. In this study, we incorporated filamentous local structures of neurons into a statistical model of image patches and then devised an image processing method based on tensor factorization with image patch rotation. Tensor factorization enabled us to incorporate correlation structure between neighboring pixels, and patch rotation helped us obtain image bases that well reproduce filamentous structures of neurons. We applied the proposed model to a microscopic image and found significant improvement in image restoration performance over existing methods, even with smaller number of bases.

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Acknowledgments

This study was supported by Grant-in-Aid for Scientific Research on Innovative Areas: ‘Mesoscopic neurocircuity: towards understanding of the functional and structural basis of brain information processing’ from MEXT, Japan.

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Authors and Affiliations

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

    Masayuki Kouno, Ken Nakae, Shigeyuki Oba & Shin Ishii

  2. PRESTO, Japan Science and Technology Agency, Saitama, Japan

    Shigeyuki Oba

Authors
  1. Masayuki Kouno
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  2. Ken Nakae
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  3. Shigeyuki Oba
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  4. Shin Ishii
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Corresponding author

Correspondence to Masayuki Kouno.

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Kouno, M., Nakae, K., Oba, S. et al. Microscopic image restoration based on tensor factorization of rotated patches. Artif Life Robotics 17, 417–425 (2013). https://doi.org/10.1007/s10015-012-0077-6

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  • DOI: https://doi.org/10.1007/s10015-012-0077-6

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