Correia, JoaoLopes, DanielVieira, LeonardoRodríguez-Fernández, NereidaCarballal, AdriánRomero, JuanMachado, Penousal2026-02-062026-02-062022Correia, J., Lopes, D., Vieira, L. et al. Experiments in evolutionary image enhancement with ELAINE. Genet Program Evolvable Mach 23, 557–579 (2022). https://doi.org/10.1007/s10710-022-09445-91389-25761573-7632https://hdl.handle.net/2183/47288“This version of the article has been accepted for publication, after peer review but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10710-022-09445-9. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms’[Abstract]: Image enhancement is an image processing procedure in which the image’s 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’s potential to transform input images into either suitable real-estate images or nonphotorealistic renderings, thus transforming contents and possibly enhancing its aesthetic appeal.engImage EnhancementImage ProcessingComputer VisionEvolutionary ComputationGenetic Programmingjournal articleopen access10.1007/s10710-022-09445-9