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

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.