Paper
4 March 2022 Virtual restoration of paintings based on deep learning
Author Affiliations +
Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 120841G (2022) https://doi.org/10.1117/12.2624371
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy
Abstract
Over time, crack pattern (craquelure) inevitably develops in paintings as a sign of their ageing, sometimes accompanied by larger losses of paint (lacunas). In restoration treatments, cracks are typically not filled in, and virtual restoration is often the only option to “reverse” the ageing of paintings, simulating their original appearance. Moreover, virtual restoration can serve as an important supporting step in decision making during the physical restoration. In this research, we investigate the possibility of applying deep learning-based methods for virtual restoration. In particular, our crack detection method is based on a convolutional autoencoder (U-Net), and we employ a generative adversarial neural network (GAN) to virtually inpaint the detected cracks. We propose an original way of training the GAN model for painting restoration, which improves its practical performance. A series of experiments shows encouraging results in comparison with known methods, and indicates huge potential of deep learning for virtual painting restoration.
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Roman Sizyakin, Viacheslav Voronin, and Aleksandra Pižurica "Virtual restoration of paintings based on deep learning", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 120841G (4 March 2022); https://doi.org/10.1117/12.2624371
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KEYWORDS
Neural networks

Image segmentation

Image processing

Image restoration

Convolutional neural networks

Image filtering

Network architectures

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