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Enhanced Image Super-Resolution Technique Using Convolutional Neural Network

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Advances in Visual Informatics (IVIC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8237))

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Abstract

A framework for image super resolution using Convolutional Neural Network (CNN) was developed. In this paper, we focus on verifying the performance of Convolutional Neural Network compared to other methods. CNN generally outperforms other super resolution methods. The training images were collected from various categories, i.e. flowers, buildings, animals, vehicles, human and cuisine. The neural network trained with multiple categories of training images made the CNN more robust towards different test scenarios. Common image degradation, i.e. motion blur and noise, can be reduced when the CNN is provided with proper training samples.

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Author information

Authors and Affiliations

  1. Centre for Computing and Intelligent Systems, Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia

    Kah Keong Chua & Yong Haur Tay

Authors
  1. Kah Keong Chua
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  2. Yong Haur Tay
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Editor information

Editors and Affiliations

  1. Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

    Halimah Badioze Zaman

  2. Computer Laboratory, University of Cambridge, CB3 0FD, Cambridge, UK

    Peter Robinson

  3. School of Computing Science, Newcastle University, Culture Lab, King’s Walk, NE1 7RU, Newcastle Upon Tyne, UK

    Patrick Olivier

  4. Dept. of Computer Science and Information Engineering, National Central University, No 300, Jhongda Rd, Jhongli, Taiwan

    Timothy K. Shih

  5. Computing and Information Systems, Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE, London, United Kingdom

    Sergio Velastin

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© 2013 Springer International Publishing Switzerland

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Chua, K.K., Tay, Y.H. (2013). Enhanced Image Super-Resolution Technique Using Convolutional Neural Network. In: Zaman, H.B., Robinson, P., Olivier, P., Shih, T.K., Velastin, S. (eds) Advances in Visual Informatics. IVIC 2013. Lecture Notes in Computer Science, vol 8237. Springer, Cham. https://doi.org/10.1007/978-3-319-02958-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-02958-0_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02957-3

  • Online ISBN: 978-3-319-02958-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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