# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
## 6 Discussions
### Limitations and Future Work  
Although this study obtained good results in restoring damaged murals, the restoration method for large-area damage remains in a preliminary exploration stage. The limitations of this study and directions for future work are outlined as follows:  
1. The dataset images were derived from manually captured photographs of Dunhuang murals, and some murals may suffer from perspective distortion. During the construction of the dataset, we eliminated such images through manual filtering and random cropping. However, this process may potentially introduce biases. For instance, since the outcomes of random cropping are uncontrollable, the cropped background images may still contain a small portion of patterns. The incomplete image information resulting from the manual filtering process may be the reason for the poor performance in the restoration of images with large-area defects. In future work, we will further optimize the dataset and explore methods for accurately extracting all pattern information from images to mitigate the negative impact of manual filtering and cropping on model performance.
2. This study has implemented model training with the storage of parameters from multiple generations of the generator and enabled the visualization of the restoration process. However, compared with conventional training tasks, the model places higher demands on the storage capacity of the computer. To enhance the applicability of the system, online storage will be integrated in the future to mitigate the storage burden on local computers. In addition, the efficient processing and analysis of these image data typically require substantial computational resources, and thus future work will require computing systems equipped with high-performance GPU.
3. The interaction of the system employs a step-by-step feedback method (as shown in Figure 11), allowing users to observe the restoration process through a sliding interface and identify the optimal restoration iteration. This study has only focused on the design of the system interface and the overall process planning, with insufficient consideration given to interface adjustment and encapsulation implementation.  
Future work will integrate knowledge from software development to further adjust and optimize the system. Additionally, future research will explore incorporating feedback from domain experts into the model to improve its efficiency and restoration performance, especially for images with large-area defects.
4. As an online platform for restoration, communication, and sharing, the system currently includes only a portion of the Dunhuang murals data. For different types of cultural heritage artworks, users and researchers would continuously expand the types of datasets (such as paintings, murals, and ancient documents). The system not only supports research on image processing and analysis methods but also has the potential to serve as a platform for testing and validating new algorithms. In the future, we will attempt to restore various types of data and integrate traditional digital-image-processing techniques to improve the robustness of the model in a broader range of restoration scenarios.
5. To address diverse restoration challenges, continuous experimentation and refinement of new methods are essential. Restoration tasks encompass not only visual information but also multimodal elements, such as the year, style, and thematic content. In subsequent research, we will focus on incorporating domain knowledge into the model-training process by integrating the complex information present in murals, thereby enhancing the overall performance of the model.