Next Article in Journal
A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance
Next Article in Special Issue
Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field
Previous Article in Journal
Antimalarial Drugs in Ghana: A Case Study on Personal Preferences
Previous Article in Special Issue
Making Japenese Ukiyo-e Art 3D in Real-Time
Article

Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks

School of Computer Science, University of St Andrews, Scotland KY16 9AJ, UK
*
Authors to whom correspondence should be addressed.
Received: 8 March 2020 / Accepted: 9 March 2020 / Published: 7 July 2020
(This article belongs to the Special Issue Machine Learning and Vision for Cultural Heritage)
In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style. View Full-Text
Keywords: deep learning; computer vision; Cycle-GAN; image reconstruction deep learning; computer vision; Cycle-GAN; image reconstruction
Show Figures

Figure 1

MDPI and ACS Style

Zachariou, M.; Dimitriou, N.; Arandjelović, O. Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks. Sci 2020, 2, 52. https://doi.org/10.3390/sci2030052

AMA Style

Zachariou M, Dimitriou N, Arandjelović O. Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks. Sci. 2020; 2(3):52. https://doi.org/10.3390/sci2030052

Chicago/Turabian Style

Zachariou, Marios, Neofytos Dimitriou, and Ognjen Arandjelović. 2020. "Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks" Sci 2, no. 3: 52. https://doi.org/10.3390/sci2030052

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop