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Singular Value Decomposition in Image Compression and Blurred Image Restoration

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Image Analysis and Recognition (ICIAR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10882))

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Abstract

The singular value decomposition (SVD) is an important and very versatile tool for matrix computations with a variety of uses. The contribution briefly introduces the concept of the SVD and basic facts about it and then describes two classes of its applications in image processing - image compression and blurred image restoration. Calculations are implemented in MATLAB software. Our experiences and the results are presented in the text.

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Acknowledgments

This work and the contribution were supported by a project of Students Grant Agency – FIM, University of Hradec Kralove, Czech Republic. Katerina Fronckova is a student member of the research team.

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Authors and Affiliations

  1. University of Hradec Kralove, Hradec Kralove, Czech Republic

    Katerina Fronckova, Pavel Prazak & Antonin Slaby

Authors
  1. Katerina Fronckova
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  2. Pavel Prazak
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  3. Antonin Slaby
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Corresponding author

Correspondence to Antonin Slaby .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Bart ter Haar Romeny

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Fronckova, K., Prazak, P., Slaby, A. (2018). Singular Value Decomposition in Image Compression and Blurred Image Restoration. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-93000-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92999-6

  • Online ISBN: 978-3-319-93000-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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