22 September 2019

Machine Learning Reconstructs Deteriorated Drawings

Researchers at TU Delft in the Netherlands have recently developed a convolutional neural network (CNN)-based model to reconstruct drawings that have deteriorated over time. In their study, published in Springer's Machine Vision and Applications, they specifically used the model to reconstruct some of Vincent Van Gogh's drawings that were ruined over the years due to ink fading and discoloration. Researchers investigated the use of machine-learning techniques for the pixel-wise reconstruction of deteriorated paintings. When it comes to art preservation, the deterioration of paintings and drawings is a key challenge, so tools that can automatically reconstruct incomplete or ruined artworks would greatly simplify the work of art historians.

They trained their CNN-based model on reproductions of deteriorated drawings by post-impressionist painter Van Gogh. Some of Van Gogh's ink drawings have deteriorated significantly over the past century, and art historians have often tried to reproduce them. These drawings cannot currently be exhibited. Researchers wanted to develop a model that can automatically reconstruct these invaluable artworks in order to preserve them and make them accessible to the public. The approach combines techniques for multi-resolution image analysis and deep CNNs to predict the past appearances of drawings pixel-wise. The algorithm was trained on a dataset containing reproductions of the original drawings of varying quality, made at different times during past century.

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