Am\'elioration de la qualit\'e d'images avec un algorithme d'optimisation inspir\'ee par la nature

• URL: http://arxiv.org/abs/2303.07151v1
• Date: Mon, 13 Mar 2023 14:25:39 GMT
• Title: Am\'elioration de la qualit\'e d'images avec un algorithme d'optimisation inspir\'ee par la nature
• Authors: Olivier Parisot and Thomas Tamisier
• Abstract summary: We propose a method to obtain an explicit and ordered sequence of transformations that improves a given image. Preliminary tests show the impact of the approach on different state-of-the-art data sets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reproducible images preprocessing is important in the field of computer vision, for efficient algorithms comparison or for new images corpus preparation. In this paper, we propose a method to obtain an explicit and ordered sequence of transformations that improves a given image: the computation is performed via a nature-inspired optimization algorithm based on quality assessment techniques. Preliminary tests show the impact of the approach on different state-of-the-art data sets. -- L'application de pr\'etraitements explicites et reproductibles est fondamentale dans le domaine de la vision par ordinateur, pour pouvoir comparer efficacement des algorithmes ou pour pr\'eparer un nouveau corpus d'images. Dans cet article, nous proposons une m\'ethode pour obtenir une s\'equence reproductible de transformations qui am\'eliore une image donn\'ee: le calcul est r\'ealis\'e via un algorithme d'optimisation inspir\'ee par la nature et bas\'e sur des techniques d'\'evaluation de la qualit\'e. Des tests montrent l'impact de l'approche sur diff\'erents ensembles d'images de l'\'etat de l'art.

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