A Study on GAN Algorithm for Restoration of Cultural Property (pagoda)

Jin-Hyun Yoon, Byong-Kwon Lee, Byung-Wan Kim
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引用次数: 1

Abstract

Today, the restoration of cultural properties is done by applying the latest IT technology from relying on existing data and experts. However, there are cases where new data are released and the original restoration is incorrect. Also, sometimes it takes too long to restore. And there is a possibility that the results will be different than expected. Therefore, we aim to quickly restore cultural properties using DeepLearning. Recently, so the algorithm DcGAN made in GANs algorithm, and image creation, restoring sectors are constantly evolving. We try to find the optimal GAN algorithm for the restoration of cultural properties among various GAN algorithms. Because the GAN algorithm is used in various fields. In the field of restoring cultural properties, it will show that it can be applied in practice by obtaining meaningful results. As a result of experimenting with the DCGAN and Style GAN algorithms among the GAN algorithms, it was confirmed that the DCGAN algorithm generates a top image with a low resolution.
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文物(宝塔)修复的GAN算法研究
如今,文化遗产的修复依靠现有的资料和专家,运用最新的信息技术(IT)进行。但是,在某些情况下,发布了新数据,而原始恢复不正确。而且,有时需要很长时间才能恢复。结果也有可能与预期不同。因此,我们的目标是使用deeplelearning快速恢复文化财产。近年来,所以在gan算法中提出的DcGAN算法,以及图像的创建、扇区的恢复等都在不断发展。我们试图在各种GAN算法中找到最适合文物修复的GAN算法。因为GAN算法在各个领域都有应用。在文物修复领域,通过取得有意义的成果,表明该方法可以应用于实践。通过对GAN算法中的DCGAN算法和Style GAN算法的实验,验证了DCGAN算法生成的顶图像分辨率较低。
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