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Experiments in evolutionary image enhancement with ELAINE

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

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Notes

  1. http://elaine.dei.uc.pt

  2. https://www.dpchallenge.com/

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Acknowledgements

This work is funded by the Foundation for Science and Technology (FCT), I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R &D Unit—UIDB /00326/2020 or project code UIDP/00326/2020 and under the grant SFRH/BD/ 143553/2019. This work is also funded by the INDITEX-UDC Program for predoctoral research stays through the Collaboration Agreement between the UDC and INDITEX for the internationalization of doctoral studies. Juan Romero received funding from Spanish Ministry of Universities for mobility stays of professors and researchers in foreign centres of higher education and research. Juan Romero and Adrian Carballal received funding with reference PID2020-118362RB-I00, from the State Program of R+D+i Oriented to the Challenges of the Society of the Spanish Ministry of Science, Innovation and Universities.

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Authors and Affiliations

  1. Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal

    João Correia, Daniel Lopes, Leonardo Vieira & Penousal Machado

  2. CITIC—Research Center of Information and Communication Technologies, University of A Coruña, Coruña, Spain

    Nereida Rodriguez-Fernandez, Adrian Carballal & Juan Romero

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  1. João Correia
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Correspondence to João Correia.

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Correia, 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/s10710-022-09445-9

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