Virtual restoration of paintings based on deep learning


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 restoratin.

Fourteenth International Conference on Machine Vision (ICMV 2021)