{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:51:36Z","timestamp":1760151096290,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,20]],"date-time":"2022-02-20T00:00:00Z","timestamp":1645315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Image Enhancement (IE) is an image processing procedure in which the image\u2019s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimize 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the framework\u2019s isolated parts.<\/jats:p>","DOI":"10.3390\/app12042212","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:14:46Z","timestamp":1645431286000},"page":"2212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Towards Automatic Image Enhancement with Genetic Programming and Machine Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5562-1996","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Correia","sequence":"first","affiliation":[{"name":"CISUC, Department of Informatics Engineering, University of Coimbra, 3030 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1412-5253","authenticated-orcid":false,"given":"Nereida","family":"Rodriguez-Fernandez","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technologies, Faculty of Communication Science, University of A Coru\u00f1a, 15071 A Coru\u00f1a, Spain"}]},{"given":"Leonardo","family":"Vieira","sequence":"additional","affiliation":[{"name":"CISUC, Department of Informatics Engineering, University of Coimbra, 3030 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5566-5382","authenticated-orcid":false,"given":"Juan","family":"Romero","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technologies, Faculty of Communication Science, University of A Coru\u00f1a, 15071 A Coru\u00f1a, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6308-6484","authenticated-orcid":false,"given":"Penousal","family":"Machado","sequence":"additional","affiliation":[{"name":"CISUC, Department of Informatics Engineering, University of Coimbra, 3030 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.14741\/Ijcet\/22774106\/5.2.2015.121","article-title":"A Review of Medical Image Enhancement Techniques for Image Processing","volume":"5","author":"Rana","year":"2011","journal-title":"Int. J. Curr. Eng. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pandey, P., Dewangan, K.K., and Dewangan, D.K. (2017, January 1\u20132). Satellite image enhancement techniques\u2014A comparative study. Proceedings of the 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India.","DOI":"10.1109\/ICECDS.2017.8389506"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1049\/iet-bmt.2016.0088","article-title":"Survey on the impact of fingerprint image enhancement","volume":"7","author":"Schuch","year":"2018","journal-title":"IET Biom."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1086\/346250","article-title":"Individual differences in the centrality of visual product aesthetics: Concept and measurement","volume":"29","author":"Bloch","year":"2003","journal-title":"J. Consum. Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chae, Y., Nakazawa, M., and Stenger, B. (2018, January 7\u201310). Enhancing product images for click-through rate improvement. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451513"},{"key":"ref_6","unstructured":"Maini, R., and Aggarwal, H. (2010). A Comprehensive Review of Image Enhancement Techniques. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.ins.2019.05.015","article-title":"Adaptive image enhancement method for correcting low-illumination images","volume":"496","author":"Wang","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_8","unstructured":"Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., and Wang, Z. (2019). EnlightenGAN: Deep Light Enhancement without Paired Supervision. arXiv."},{"key":"ref_9","first-page":"82","article-title":"Evolving Image Enhancement Pipelines","volume":"Volume 12693","author":"Romero","year":"2021","journal-title":"Artificial Intelligence in Music, Sound, Art and Design, Proceedings of the 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Virtual Event, 7\u20139 April 2021"},{"key":"ref_10","unstructured":"Umbaugh, S.E. (1997). Computer Vision and Image Processing: A Practical Approach Using Cviptools with Cdrom, Prentice Hall PTR. [1st ed.]."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Laplante, P.A. (2018). Perceptual Image Enhancement. Encyclopedia of Image Processing, CRC Press.","DOI":"10.1201\/9781351032742"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhuo, S., Zhang, X., Miao, X., and Sim, T. (2010, January 26\u201329). Enhancing low light images using near infrared flash images. Proceedings of the International Conference on Image Processing, ICIP, Hong Kong, China.","DOI":"10.1109\/ICIP.2010.5652900"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Talebi, H., and Milanfar, P. (2016). Fast Multi-Layer Laplacian Enhancement. IEEE Trans. Comput. Imaging.","DOI":"10.1109\/TCI.2016.2607142"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1016\/j.jvcir.2016.04.019","article-title":"Histogram equalization and optimal profile compression based approach for colour image enhancement","volume":"38","author":"Wong","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Afifi, M., Punnappurath, A., Abdelhamed, A., Karaimer, H.C., Abuolaim, A., and Brown, M.S. (2019). Color Temperature Tuning: Allowing Accurate Post-Capture White-Balance Editing. Color Imaging Conference (CIC), Society for Imaging Science and Technology.","DOI":"10.2352\/issn.2169-2629.2019.27.2"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Afifi, M., and Brown, M.S. (2020, January 14\u201319). Deep White-Balance Editing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00147"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Deng, Y., Loy, C.C., and Tang, X. (2018, January 22\u201326). Aesthetic-driven image enhancement by adversarial learning. Proceedings of the 2018 ACM Multimedia Conference (MM 2018), Seoul, Korea.","DOI":"10.1145\/3240508.3240531"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, Q., Xu, J., and Koltun, V. (2018, January 18\u201322). Learning to See in the Dark. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00347"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kang, S.B., Kapoor, A., and Lischinski, D. (2010, January 13\u201318). Personalization of image enhancement. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539850"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., and Efros, A.A. (2016, January 27\u201330). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1006\/cviu.1996.0060","article-title":"Fast Noise Variance Estimation","volume":"64","year":"1996","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1364\/JOSAA.7.002032","article-title":"Contrast in complex images","volume":"7","author":"Peli","year":"1990","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_24","unstructured":"Rex Finley, D. (2022, January 20). HSP Color Model\u2014Alternative to HSV (HSB) and HSL. Available online: http:\/\/alienryderflex.com\/hsp.html."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pech-Pacheco, J.L., Cristobal, G., Chamorro-Martinez, J., and Fernandez-Valdivia, J. (2000, January 3\u20137). Diatom autofocusing in brightfield microscopy: A comparative study. Proceedings of the 15th International Conference on Pattern Recognition, ICPR-2000, Barcelona, Spain.","DOI":"10.1109\/ICPR.2000.903548"},{"key":"ref_26","first-page":"2171","article-title":"DEAP: Evolutionary Algorithms Made Easy","volume":"13","author":"Fortin","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_27","unstructured":"Bazeille, S., Quidu, I., Jaulin, L., and Malkasse, J.P. (2006, January 16\u201319). Automatic underwater image pre-processing. Proceedings of the CMM\u201906, Brest, France."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"012161","DOI":"10.1088\/1742-6596\/1314\/1\/012161","article-title":"Image Enhancement Based on Histogram Equalization","volume":"1314","author":"Xie","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"11782","DOI":"10.1109\/ACCESS.2018.2797872","article-title":"Automatic Contrast-Limited Adaptive Histogram Equalization with Dual Gamma Correction","volume":"6","author":"Chang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"208","DOI":"10.5201\/ipol.2011.bcm_nlm","article-title":"Non-Local Means Denoising","volume":"1","author":"Buades","year":"2011","journal-title":"Image Process. Online"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"297","DOI":"10.5201\/ipol.2011.llmps-scb","article-title":"Simplest Color Balance","volume":"1","author":"Limare","year":"2011","journal-title":"Image Process. Online"},{"key":"ref_32","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems 27, Curran Associates, Inc."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rodriguez-Fernandez, N., Alvarez-Gonzalez, S., Santos, I., Torrente-Pati\u00f1o, A., Carballal, A., and Romero, J. (2022). Validation of an Aesthetic Assessment System for Commercial Tasks. Entropy, 24.","DOI":"10.3390\/e24010103"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2374","DOI":"10.1080\/00207160.2013.816415","article-title":"Objective image quality assessment: A survey","volume":"91","author":"He","year":"2014","journal-title":"Int. J. Comput. Math."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-Reference Image Quality Assessment in the Spatial Domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.jvcir.2017.02.016","article-title":"Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition","volume":"45","author":"Lim","year":"2017","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, G., Li, L., Li, Q., Gu, K., Lu, Z., and Qian, J. (2017, January 17\u201320). Perceptual evaluation of single-image super-resolution reconstruction. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296862"},{"key":"ref_38","first-page":"3998","article-title":"NIMA: Neural Image Assessment","volume":"27","author":"Esfandarani","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1023\/A:1013662402341","article-title":"All the Truth About NEvAr","volume":"16","author":"Machado","year":"2002","journal-title":"Appl. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Correia, J., Martins, T., and Machado, P. (2019, January 13\u201317). Evolutionary Data Augmentation in Deep Face Detection. Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO 2019), Prague, Czech Republic.","DOI":"10.1145\/3319619.3322053"}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/12\/4\/2212\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:23:35Z","timestamp":1760135015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/12\/4\/2212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,20]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["app12042212"],"URL":"https:\/\/doi.org\/10.3390\/app12042212","relation":{},"ISSN":["2076-3417"],"issn-type":[{"type":"electronic","value":"2076-3417"}],"subject":[],"published":{"date-parts":[[2022,2,20]]}}}