Dunhuang Mural Restoration using Deep Learning

• Main Authors: Han-Lei Wang, 王瀚磊
• Other Authors: 李明穗
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/xcuc47

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 106 === The art of Dunhuang Grottoes is mainly composed of three forms: building, sculpture, and murals. With the passage of time, the art of Dunhuang Grottoes has suffered from tremendous damage, and making it difficult to repair. With Virtual Reality, we can even observe the digital preservation of Dunhuang Caves in virtual environment as if we are in Dunhuang by modeling the caves and preserve mural as textures. However, we still cannot have a glimpse of how the grottoes look like without damage. With the ever-changing nature of deep learning, it is widely used in many fields. In this work, we propose a systematic restoration process for high-resolution types of deteriorated mural textures. We use the Generative Adversarial Network technology in deep learning. By making the machine learn the transformation between deteriorated mural textures and restored mural textures, we can simultaneously produce high-resolution but color-inconsistent, as well as low-resolution but color-consistent results. After post-processing through digital image processing technology, we can get both high-resolution and color-consistent texture.