Experiments in evolutionary image enhancement with ELAINE

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Correia:GPEM,

• author = "Joao Correia and Daniel Lopes and Leonardo Vieira and Nereida Rodriguez-Fernandez and Adrian Carballal and Juan Romero and Penousal Machado",
• title = "Experiments in evolutionary image enhancement with {ELAINE}",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2022",
• volume = "23",
• number = "4",
• pages = "557--579",
• month = dec,
• note = "Special Issue: Evolutionary Computation in Art, Music and Design",
• keywords = "genetic algorithms, genetic programming, Image enhancement, Image processing, Computer vision, Evolutionary computation,",
• ISSN = "1389-2576",
• URL = "https://rdcu.be/cYSxw",
• DOI = "doi:10.1007/s10710-022-09445-9",
• size = "23 pages",
• abstract = "Image enhancement is an image processing procedure in which the image original information is refined, for example by highlighting specific features to ease post-processing analyses by a human or machine. This procedure remains challenging since each set of images is often taken under diverse conditions which makes it hard to find an image enhancement solution that fits all conditions. State-of-the-art image enhancement pipelines apply filters that solve specific issues; therefore, it is still hard to generalise these pipelines to all types of problems encountered. We have recently introduced a Genetic Programming approach named ELAINE (EvoLutionAry Image eNhancEment) for evolving image enhancement pipelines based on pre-defined image filters. In this paper, we showcase its potential to create solutions under a real-estate marketing scenario by comparing it with a manual approach and an existing tool for automatic image enhancement. The ELAINE obtained results far exceed those obtained by manual combinations of filters and by the one-click method, in all the metrics explored. We further explore the potential of creating non-photorealistic effects by applying the evolved pipelines to different types of images. The results highlight ELAINE potential to transform input images into either suitable real-estate images or non-photorealistic renderings, thus transforming contents and possibly enhancing its aesthetic appeal.",
• notes = "Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal",

}

Genetic Programming entries for Joao Nuno Goncalves Costa Cavaleiro Correia Daniel Lopes Leonardo Vieira Nereida Rodriguez-Fernandez Adrian Carballal Mato Juan Jesus Romero Cardalda Penousal Machado

Citations