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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