摘要：Through the research of numerous researchers’ artificial intelligence imitates human language and visual expression with good performance and imitates human style in voice and picture. This ability although dependent on the data for learning artificial intelligence is more objective and based on numerical data than humans. We applied it to the restoration of cultural assets made in the past through artificial intelligence neural networks and we applied a general CNN a little differently for the purpose of restoration. Cultural properties contain various backgrounds from the era when they were created and for this reason there are many complications and difficulties in restoration. If it is simply regarded as noise and recovered the result is dependent on the learned data. To solve this problem the CNN was separated into full and detailed and the association was learned together and the damaged part was repaired through a generative competition network (GAN) based on this neural network. We trained a neural network that extracts visual features on a Korean "Pagoda" (mostly produced under the influence of Buddhism) and conducted a study to repair the damaged part based on the trained neural network. The features of the tower were extracted through a CNN-based neural network and the damaged part was repaired through a Generative Adversarial Network (GAN) based on the extracted features. It is thought that our research will be actively used for the restoration of cultural assets as well as the restoration of archaeological records in the future.
