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Reproducible Improvement of Images Quality Through Nature Inspired Optimisation

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Cooperative Design, Visualization, and Engineering (CDVE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12983))

Abstract

Transparent and reproducible image processing is fundamental in science and engineering, whether to prove the efficiency of an algorithm or to prepare a new corpus of images. In this paper, we propose a Nature Inspired Optimisation algorithm based on Image Quality Assessment methods to obtain a reproducible sequence of transformations that improves the quality of a given image. Preliminary tests were realized on state-of-the-art benchmarks.

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Notes

  1. 1.

    https://pypi.org/project/opencv-python/.

  2. 2.

    https://pypi.org/project/scikit-image/.

  3. 3.

    https://pypi.org/project/image-quality/.

  4. 4.

    https://github.com/idealo/image-quality-assessment.

  5. 5.

    https://tinyurl.com/falconheavyrocket.

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Acknowledgements

this work was realized by using the LIST Cognitive Pillar, a high performance infrastructure funded by the Data Analytics Platform project (http://tiny.cc/feder-dap-project). Special thanks to A. Hendrick, S. Renault and R. Jadoul for their support.

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Authors and Affiliations

  1. Luxembourg Institute of Science and Technology (LIST), 5 Avenue des Hauts-Fourneaux, 4362, Esch-sur-Alzette, Luxembourg

    Olivier Parisot & Thomas Tamisier

Authors
  1. Olivier Parisot
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  2. Thomas Tamisier
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Corresponding author

Correspondence to Olivier Parisot .

Editor information

Editors and Affiliations

  1. University of the Balearic Islands, Palma, Mallorca, Spain

    Yuhua Luo

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Parisot, O., Tamisier, T. (2021). Reproducible Improvement of Images Quality Through Nature Inspired Optimisation. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2021. Lecture Notes in Computer Science(), vol 12983. Springer, Cham. https://doi.org/10.1007/978-3-030-88207-5_33

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  • DOI: https://doi.org/10.1007/978-3-030-88207-5_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88206-8

  • Online ISBN: 978-3-030-88207-5

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