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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2019: Intelligent Data Engineering and Automated Learning – IDEAL 2019 pp 247–256Cite as

Image Quality Constrained GAN for Super-Resolution

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11871))

Abstract

As one of the most important research topics in image processing, super-resolution aims to estimate high resolution images from single or multiple low resolution images taken from the same scene. With the advent of deep learning techniques, generative adversarial networks are widely adopted for solving various image processing problems including super resolution. We investigate the effect of introducing image quality constraints into the training objective function of a generative adversarial network for super resolution. Experiment results demonstrate that network performance has great potential to be improved with such constraints.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, M13 9PL, UK

    Jingwen Su, Yao Peng & Hujun Yin

Authors
  1. Jingwen Su
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  2. Yao Peng
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  3. Hujun Yin
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Corresponding author

Correspondence to Jingwen Su .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  5. University of Exeter, Exeter, UK

    Ronaldo Menezes

  6. University of Manchester, Manchester, UK

    Richard Allmendinger

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Cite this paper

Su, J., Peng, Y., Yin, H. (2019). Image Quality Constrained GAN for Super-Resolution. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_27

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  • DOI: https://doi.org/10.1007/978-3-030-33607-3_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33606-6

  • Online ISBN: 978-3-030-33607-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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