# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
## 1 Introduction  
Murals are an important part of Dunhuang culture; the murals cover a total area of more than 50,000 m\({}^{2}\), with a huge span of dynasties and rich forms of expression. Digital preservation [1,2] allows murals to be measured in three dimensions and recorded digitally. This is one of the best methods used to ensure their permanent preservation. After more than 2000 years of rainfall, Dunhuang murals urgently require restoration, as several exhibit peeling, fading, and cracks. Early restoration work primarily relied on manual labor. Restorers were required to have a high level of expertise, which made professionals scarce. Moreover, the time cost of manual restoration is high, and other problems, such as high risk and irreversibility, must be addressed.  
With the rapid development of computer technology, artificial intelligence (AI) has become deeply involved in the conservation, restoration, and archeology of cultural relics.  
Examples include the recognition of ancient words [3], automatic recognition of archeological pottery [4], restoration of image colors and textures, and automatic generation of ancient mural images [5,6]. Currently, the Dunhuang Research Institute restores degraded areas via virtual restoration with deep learning models. However, for more complex cases, the structure of the output results or generated edges may not satisfy the requirements of professionals. Subsequently, the use of the EdgeConnect [7] network was proposed to generate optimal structural edges and textures. All of these works achieved the reversibility of restoration work, which is significant in the conservation of Dunhuang murals.  
This study focuses on the restoration tasks of two types of mural defects, including center regular defects and random irregular defects. Center regular defects mean that the mural has a large area of defects with a regular shape. Random irregular defects mean that the defects are dispersed and each damage area is small. For small defect areas, existing models can learn and infer the morphology of a defect from an intact area. For a large defect area, the restoration results are characterized by fuzziness and distortion. Professionals and computers collaborate to obtain high-quality restorative results.  
To achieve this, we constructed a dataset of Dunhuang murals and developed an image restoration model. The model uses a shift-net connection layer based on a U-net architecture to extract information on global semantics and detailed features of the images. The results demonstrate that the model is capable of accurately restoring murals with non-central random damage. By filtering images for noise reduction and contour extraction and employing the intermediate model in the training process, we demonstrated the restoration process of Dunhuang murals. The main contributions of this study are as follows:  
1. We developed an automated restoration model that achieves good results in restoring random irregular defective areas of murals.
2. We propose an image-process-handling method for visualizing the mural restoration process.
3. We establish a human-machine collaborative restoration platform for professionals that significantly improves work efficiency and enables the online sharing and retention of restoration results.  
The remainder of this paper is organized as follows: Section 2 reviews related research. Section 3 introduces the dataset construction method and implementation method of the modeling technique. Section 4 presents the experiments and results. Section 5 discusses the design of the online platform for the mural restoration system, and Section 6 discusses the model and its applications. Finally, Section 7 concludes the paper.