Abstract
Many works of Van Gogh’s oeuvre, such as letters, drawings and paintings, have been severely degraded due to light exposure. Digital reconstruction of faded color can help to envisage how the artist’s work may have looked at the time of creation. In this paper, we study the reconstruction of Vincent van Gogh’s drawings by means of learning schemes and on the basis of the available reproductions of these drawings. In particular, we investigate the use of three machine learning algorithms, k-nearest neighbor (kNN) estimation, linear regression (LR), and convolutional neural networks (CNN), for learning the reconstruction of these faded drawings. Experimental results show that the reconstruction performance of the kNN method is slightly better than those of the CNN. The reconstruction performance of the LR is much worse than those of the kNN and the CNN.
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Acknowledgments
This research is a part of the REVIGO project, supported by the Netherlands Organisation for scientific research (NWO; grant 323.54.004) in the context of the Science4Arts research program.
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Zeng, Y., Tang, J., van der Lubbe, J.C.A., Loog, M. (2016). Learning Algorithms for Digital Reconstruction of Van Gogh’s Drawings. In: Ioannides, M., et al. Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection. EuroMed 2016. Lecture Notes in Computer Science(), vol 10058. Springer, Cham. https://doi.org/10.1007/978-3-319-48496-9_26
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DOI: https://doi.org/10.1007/978-3-319-48496-9_26
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