Abstract
Image enhancement is an image processing procedure in which the original information of the image is improved. It alters an image in several different ways, for instance, by highlighting a specific feature in order to ease post-processing analyses by a human or machine. In this work, we show our approach to image enhancement for digital real-estate-marketing. The aesthetic quality of the images for real-estate marketing is critical since it is the only input clients have once browsing for options. Thus, improving and ensuring the aesthetic quality of the images is crucial for marketing success. The problem is that each set of images, even for the same real-estate item, is often taken under diverse conditions making it hard to find one solution that fits all. State of the art image enhancement pipelines applies a set of filters that solve specific issues, so it is still hard to generalise that solves all types of issues encountered. With this in mind, we propose a Genetic Programming approach for the evolution of image enhancement pipelines, based on image filters from the literature. We report a set of experiments in image enhancement of real state images and analysed the results. The overall results suggest that it is possible to attain suitable pipelines that visually enhance the image and according to a set of image quality assessment metrics. The evolved pipelines show improvements across the validation metrics, showing that it is possible to create image enhancement pipelines automatically. Moreover, during the experiments, some of the created pipelines create non-photorealistic rendering effects in a moment of computational serendipity. Thus, we further analysed the different evolved non-photorealistic solutions, showing the potential of applying the evolved pipelines in other types of images.
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References
Esfandarani, H.T., Milanfar, P.: NIMA: neural image assessment. CoRR abs/1709.05424 (2017). http://arxiv.org/abs/1709.05424
Pease, A., Colton, S., Ramezani, R., Charnley, J., Reed, K.: A discussion on serendipity in creative systems. In: Maher, M., Veale, T., Saunders, R., Bown, O. (eds.) Proceedings of the 4th International Conference on Computational Creativity, ICCC 2013, 12 June 2013 Through 14 June 2013, pp. 64–71. University of Sydney, Faculty of Architecture, Design and Planning (2013). http://www.computationalcreativity.net/iccc2013/
Wang, W., Chen, Z., Yuan, X., Wu, X.: Adaptive image enhancement method for correcting low-illumination images. Inf. Sci. 496, 25–41 (2019)
Wong, C.Y., et al.: Histogram equalization and optimal profile compression based approach for colour image enhancement. J. Vis. Commun. Image Represent. 38, 802–813 (2016) http://dx.doi.org/10.1016/j.jvcir.2016.04.019
Talebi, H., Milanfar, P.: Fast multi-layer laplacian enhancement. IEEE Trans. Comput. Imaging (2016)
Zhuo, S., Zhang, X., Miao, X., Sim, T.: Enhancing low light images using near infrared flash images. In: Proceedings - International Conference on Image Processing, ICIP, pp. 2537–2540 (2010)
Rundo, L., et al.: MedGA: a novel evolutionary method for image enhancement in medical imaging systems. Expert Syst. Appl. 119, 387–399 (2018)
Munteanu, C., Rosa, A.: Evolutionary image enhancement with user behaviour modeling. ACM SIGAPP Appl. Comput. Rev. 9, 8–14 (2000)
Shan, T., Wang, S., Zhang, X., Jiao, L.: Automatic image enhancement driven by evolution based on ridgelet frame in the presence of noise. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 304–313. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-32003-6_31
Bazeille, S., Quidu, I., Jaulin, L., Malkasse, J.P.: Automatic underwater image pre-processing. In: Proceedings of CMM 2006, October 2006
Xie, Y., Ning, L., Wang, M., Li, C.: Image enhancement based on histogram equalization. J. Phys.: Conf. Ser. 1314, 012161 (2019)
Chang, Y., Jung, C., Ke, P., Song, H., Hwang, J.: Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access 6, 11782–11792 (2018)
Buades, A., Coll, B., Morel, J.M.: Non-local means denoising. Image Process. Line 1, 208–212 (2011)
Deng, Y., Loy, C.C., Tang, X.: Aesthetic-driven image enhancement by adversarial learning. In: MM 2018 - Proceedings of the 2018 ACM Multimedia Conference, pp. 870–878 (2018)
Limare, N., Lisani, J.L., Morel, J.M., Petro, A.B., Sbert, C.: Simplest color balance. Image Process. Line 1, 297–315 (2011)
Immerkær, J.: Fast noise variance estimation. Comput. Vis. Image Underst. 64(2), 300–302 (1996). https://doi.org/10.1006/cviu.1996.0060
Peli, E.: Contrast in complex images. J. Opt. Soc. Am. A 7(10), 2032–2040 (1990)
Rex Finley, D.: HSP color model - alternative to HSV (HSB) and HSL (2006). http://alienryderflex.com/hsp.html
Pech-Pacheco, J.L., Cristobal, G., Chamorro-Martinez, J., Fernandez-Valdivia, J.: Diatom autofocusing in brightfield microscopy: a comparative study. In: Proceedings 15th International Conference on Pattern Recognition, ICPR-2000, vol. 3, pp. 314–317 (2000)
Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)
He, L., Gao, F., Hou, W., Hao, L.: Objective image quality assessment: a survey. Int. J. Comput. Math. 91(11), 2374–2388 (2014). https://doi.org/10.1080/00207160.2013.816415
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)
Lim, J., Heo, M., Lee, C., Kim, C.S.: Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition. J. Vis. Commun. Image Represent. 45, 107–121 (2017). http://www.sciencedirect.com/science/article/pii/S1047320317300603
Wang, G., Li, L., Li, Q., Gu, K., Lu, Z., Qian, J.: Perceptual evaluation of single-image super-resolution reconstruction. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3145–3149 (2017)
Banzhaf, W., Francone, F.D., Keller, R.E., Nordin, P.: Genetic Programming: An Introduction: On the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann Publishers Inc., San Francisco (1998)
Acknowledgments
This work is funded by national funds through the FCT - Foundation for Science and Technology, I.P., in the scope of the project CISUC - UID/CEC/00326/2020 and by European Social Fund, through the Regional Operational Program Centro 2020. 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.
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Correia, J., Vieira, L., Rodriguez-Fernandez, N., Romero, J., Machado, P. (2021). Evolving Image Enhancement Pipelines. In: Romero, J., Martins, T., Rodríguez-Fernández, N. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2021. Lecture Notes in Computer Science(), vol 12693. Springer, Cham. https://doi.org/10.1007/978-3-030-72914-1_6
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