Applying Genetic Algorithm and Image Quality Assessment for Reproducible Processing of Low-light Images

Olivier Parisot; Thomas Tamisier

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Paper

Applying Genetic Algorithm and Image Quality Assessment for Reproducible Processing of Low-light Images

Topics: Computer Vision Algorithms; Image Restoration

In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering IMPROVE - Volume 1, 189-194, 2022

Authors: Olivier Parisot and Thomas Tamisier

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

Keyword(s): Low-light Images, Image Quality Assessment, Genetic Algorithms.

Abstract: Reproducible images preprocessing is fundamental in computer vision, whether to fairly compare process algorithms or to prepare new images corpus. In this paper, we propose an approach based on genetic algorithm combined to Image Quality Assessment methods to obtain a reproducible sequence of transformations for improving low-light images. Preliminary tests have been performed on state-of-the-art benchmarks.

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Paper citation in several formats:

Parisot, O. and Tamisier, T. (2022). Applying Genetic Algorithm and Image Quality Assessment for Reproducible Processing of Low-light Images. In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE; ISBN 978-989-758-563-0; ISSN 2795-4943, SciTePress, pages 189-194. DOI: 10.5220/0011082400003209

@conference{improve22,
author={Olivier Parisot. and Thomas Tamisier.},
title={Applying Genetic Algorithm and Image Quality Assessment for Reproducible Processing of Low-light Images},
booktitle={Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE},
year={2022},
pages={189-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011082400003209},
isbn={978-989-758-563-0},
issn={2795-4943},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE
TI - Applying Genetic Algorithm and Image Quality Assessment for Reproducible Processing of Low-light Images
SN - 978-989-758-563-0
IS - 2795-4943
AU - Parisot, O.
AU - Tamisier, T.
PY - 2022
SP - 189
EP - 194
DO - 10.5220/0011082400003209
PB - SciTePress