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首页> 外文会议>International conference on progress in cultural heritage preservation >Learning Algorithms for Digital Reconstruction of Van Gogh's Drawings
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Learning Algorithms for Digital Reconstruction of Van Gogh's Drawings

机译：梵高制图数字重建的学习算法

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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.
机译：梵高的许多作品，如信件，素描和绘画，由于曝光而严重退化。褪色的数字重建可以帮助设想艺术家在创作时的作品。在本文中，我们通过学习方案并根据这些图纸的可用复制品，研究了Vincent Van Gogh的图纸的重建。特别是，我们研究了三种机器学习算法的使用，即k近邻（kNN）估计，线性回归（LR）和卷积神经网络（CNN），以学习这些退色图纸的重建。实验结果表明，kNN方法的重建性能略优于CNN。 LR的重建性能比kNN和CNN的重建性能差得多。

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