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Improving Adversarial Learning with Image Quality Measures for Image Deblurring

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12489))

Abstract

Generative adversarial networks (GANs) have become popular and powerful models for solving a wide range of image processing problems. We introduce a novel component based on image quality measures in the objective function of GANs for solving image deblurring problems. Such additional constraints can regularise the training and improve the performance. Experimental results demonstrate marked improvements on generated or restored image quality both quantitatively and visually. Boosted model performances are observed and testified on three test sets with four image quality measures. It shows that image quality measures are additional flexible, effective and efficient loss components to be adopted in the objective function of GANs.

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Authors and Affiliations

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

    Jingwen Su & Hujun Yin

Authors
  1. Jingwen Su
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  2. Hujun Yin
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Corresponding authors

Correspondence to Jingwen Su or Hujun Yin .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Su, J., Yin, H. (2020). Improving Adversarial Learning with Image Quality Measures for Image Deblurring. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_15

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  • DOI: https://doi.org/10.1007/978-3-030-62362-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62361-6

  • Online ISBN: 978-3-030-62362-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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