Utilization of generative adversarial networks (GANs) in the replication and restoration of calligraphy art
Abstract
With the continuous integration of culture and technology, the replication and restoration of calligraphy art has become a new direction in the restoration of traditional calligraphy and painting. The research on the replication and restoration of calligraphy art is of great significance in contemporary art. However, the accuracy and authenticity of the replicas in the process of replication and restoration calligraphy art are not high, and there are significant differences in material selection. This paper started with GAN (Generative Adversarial Network), collected a large amount of image data from calligraphy art works, trained a generative adversarial network, and used the trained model to process calligraphy art works. Compared with traditional replication techniques, the cost, restoration efficiency, and cultural inheritance and recognition of the two in calligraphy art replication were analyzed. Research has found that the highest score for the restoration of calligraphy art quality using traditional methods can only reach 89.9 points (out of a total of 100 points), while the highest score for GAN can reach 96.4 points. Moreover, the application of GAN can save the cost of calligraphy art for restoration, improve restoration efficiency, and enable flexible application in different scenarios.
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