Learning Algorithms for Digital Reconstruction of Van Gogh’s Drawings

Yuan Zeng, Jiexiong Tang, Jan van der Lubbe, Marco Loog

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationEuroMed 2016 - 6th International Conference - Proceedings
Subtitle of host publicationDigital Heritage - Progress in Cultural Heritage: Documentation, Preservation, and Protection
EditorsMarinos Ioannides, Eleanor Vink, Antonia Moropoulou, Monika Hagedorn-Saupe, Antonella Fresa, Gunnar Liestøl, Vlatka Rajcic, Pierre Grussenmeyer
Place of PublicationCham
PublisherSpringer Science+Business Media
Number of pages12
ISBN (Electronic)978-3-319-48496-9
ISBN (Print)978-3-319-48495-2
Publication statusPublished - 2016
EventEuroMed 2016: International Conference on Digital Heritage - Nicosia, Cyprus
Duration: 31 Oct 20165 Nov 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuroMed 2016


  • Van Gogh’s drawing
  • Drawing reconstruction
  • Reproduction
  • Machine learning


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