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

Olivier Parisot, Thomas Tamisier

2022

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.

Paper Citation

in Harvard Style

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 - Volume 1: IMPROVE, ISBN 978-989-758-563-0, pages 189-194. DOI: 10.5220/0011082400003209

in Bibtex Style

@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 - Volume 1: IMPROVE,},
year={2022},
pages={189-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011082400003209},
isbn={978-989-758-563-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Applying Genetic Algorithm and Image Quality Assessment for Reproducible Processing of Low-light Images
SN - 978-989-758-563-0
AU - Parisot O.
AU - Tamisier T.
PY - 2022
SP - 189
EP - 194
DO - 10.5220/0011082400003209