A First Glance into Reversing Senescence on Herbarium Sample Images Through Conditional Generative Adversarial Networks

  • Juan Villacis-LlobetEmail author
  • Marco Lucio-Troya
  • Marvin Calvo-Navarro
  • Saul Calderon-Ramirez
  • Erick Mata-Montero
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1087)


In this paper we describe a novel approach to perform senescense reversal on photos of leaves based on Conditional Generative Adversarial Networks, which have been used succesfully to perform similar tasks on faces of humans and other picture to picture translations. We show that their use can lead to a valid solution to this problem, as long as the task of creating a large and comprehensive dataset is surpassed. Additionally, we present a new dataset that consists of 120 paired photos of leaves manually collected for this work, in their fresh and senescenced states. We used the structure similarity index to compare the ground truth with the generated images and yielded an average of 0.9.


Senescence Herbaria Conditional-GANs Bioinformatics 


  1. 1.
    Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: Advances in Neural Information Processing Systems, pp. 658–666 (2016)Google Scholar
  2. 2.
    Antipov, G., Baccouche, M., Dugelay, J.L.: Face aging with conditional generative adversarial networks. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2089–2093. IEEE (2017)Google Scholar
  3. 3.
    Candussi, A., Candussi, N., Höllerer, T.: Rendering realistic trees and forests in real time. In: Eurographics (Short Presentations), pp. 73–76. Citeseer (2005)Google Scholar
  4. 4.
    Carranza-Rojas, J., Goeau, H., Bonnet, P., Mata-Montero, E., Joly, A.: Going deeper in the automated identification of Herbarium specimens. BMC Evol. Biol. 17(1), 181 (2017)CrossRefGoogle Scholar
  5. 5.
    Carranza-Rojas, J., Mata-Montero, E.: On the significance of leaf sides in automatic leaf-based plant species identification. In: 2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI), pp. 1–6, November 2016.
  6. 6.
    Chi, X., Sheng, B., Chen, Y., Wu, E.H.: Physically based simulation of weathering plant leaves. Chin. J. Comput. 32, 221–230 (2009)CrossRefGoogle Scholar
  7. 7.
    Gaston, K.J., O’Neill, M.A.: Automated species identification: why not? Philos. Trans. R. Soc. Lond. Ser. B: Biol. Sci. 359(1444), 655–667 (2004)CrossRefGoogle Scholar
  8. 8.
    Goëau, H., et al.: PlantNet participation at lifeclef2014 plant identification task. In: CLEF2014 Working Notes. Working Notes for CLEF 2014 Conference, Sheffield, UK, 15–18 September 2014, pp. 724–737. CEUR-WS (2013)Google Scholar
  9. 9.
    Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  10. 10.
    Guan, R., Wan, Y.: An improved unsharp masking sharpening algorithm for image enhancement. In: Eighth International Conference on Digital Image Processing (ICDIP 2016), vol. 10033, p. 100332A. International Society for Optics and Photonics (2016)Google Scholar
  11. 11.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)Google Scholar
  12. 12.
    Rolland, J.P., Vo, V., Bloss, B., Abbey, C.K.: Fast algorithms for histogram matching: application to texture synthesis. J. Electron. Imaging 9(1), 39–45 (2000)CrossRefGoogle Scholar
  13. 13.
    Jeong, S., Park, S.H., Kim, C.H.: Simulation of morphology changes in drying leaves. In: Computer Graphics Forum, vol. 32, pp. 204–215. Wiley Online Library (2013)Google Scholar
  14. 14.
    Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks (2017)Google Scholar
  15. 15.
    Mata-Montero, E., Carranza-Rojas, J.: Automated plant species identification: challenges and opportunities. In: Mata, F.J., Pont, A. (eds.) WITFOR 2016. IAICT, vol. 481, pp. 26–36. Springer, Cham (2016). Scholar
  16. 16.
    Miao, T., Zhao, C., Guo, X., Lu, S., Wen, W., et al.: Simulation of plant leaf color based on relative content of chlorophyll. Nongye Jixie Xuebao = Trans. Chin. Soc. Agric. Mach. 45(8), 282–287 (2014)Google Scholar
  17. 17.
    Silva, P., Yue, Y., Chen, B.Y., Nishita, T.: Simulating plant color aging taking into account the sap flow in the venation (2012)Google Scholar
  18. 18.
    Wang, Z., Bovik, A.C.: Mean squared error: Love it or leave it? A new look at signal fidelity measures. IEEE Signal Process. Mag. 26(1), 98–117 (2009)CrossRefGoogle Scholar
  19. 19.
    Wang, Z., Tang, X., Luo, W., Gao, S.: Face aging with identity-preserved conditional generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7939–7947 (2018)Google Scholar
  20. 20.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Juan Villacis-Llobet
    • 1
    Email author
  • Marco Lucio-Troya
    • 1
  • Marvin Calvo-Navarro
    • 1
  • Saul Calderon-Ramirez
    • 1
  • Erick Mata-Montero
    • 1
  1. 1.Instituto Tecnológico de Costa RicaCartagoCosta Rica

Personalised recommendations