Experiments in evolutionary image enhancement with ELAINE

• Published in: Genetic programming and evolvable machines Vol. 23; no. 4; pp. 557 - 579
• Main Authors: Correia, João, Lopes, Daniel, Vieira, Leonardo, Rodriguez-Fernandez, Nereida, Carballal, Adrian, Romero, Juan, Machado, Penousal
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Biomedical Engineering and Bioengineering; Compilers; Computer Science; Electrical Engineering; Genetic algorithms; Image enhancement; Image filters; Image processing; Interpreters; Music and Design; Programming Languages; Programming Techniques; Software Engineering/Programming and Operating Systems; Special Issue: Evolutionary Computation in Art; Computer vision; Image processing; Genetic programming; Image enhancement; Evolutionary computation
• ISSN: 1389-2576; 1573-7632
• DOI: 10.1007/s10710-022-09445-9

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 non-photorealistic renderings, thus transforming contents and possibly enhancing its aesthetic appeal.