# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
## 2 Related Works
### Image Restoration Methods Based on Deep Learning  
Deep learning-based image restoration methods primarily rely on neural networks to restore images. The experiment trains, and the generator is near the sample probability distribution by sampling the potential space. This method can learn morphological information from the intact region of an image and obtain morphological information in a dataset comprising similar images. A large range of image defects can be restored; however, the restoration effect is closely related to the reference data.  
[PERSON] et al. [23] proposed a method that combined convolutional networks and autoencoders for image denoising. Laying the foundation for learning image-processing architectures with a high degree of representational power. Subsequently, [PERSON] et al. [24] proposed a deep network that employs a sparse encoder and denoising autoencoder to handle image and text noise. [PERSON] et al. [25] proposed the use of a modified linear function as an activation function in the hidden layer to improve the restoration effect and convergence speed of the model. [PERSON] et al. [26] proposed a synthesis method based on the joint optimization of image content and texture constraints. The method achieved good results in high-resolution image processing. [PERSON] et al. [27] learned image features based on a deep generative model to improve the restoration quality. An end-to-end Dunhuang mural image restoration model was proposed. [PERSON] et al. [28] used the patch-swap operation to optimize semantic texture and restore areas. A learning-based method is proposed to enable the generation of visually coherent complements for defective images.  
The image restoration model aims to restore reasonably defective content. The content must be consistent with the surrounding regions in terms of artistic style, semantic perception, and texture distribution. In this regard, [PERSON] et al. [29] made the first attempt to use a generative adversarial network (GAN) to restore a wide range of defects in an image. However, a clear stratification remained between restored and undamaged areas. [PERSON] et al. [30] used a GAN framework with two discriminators to correctly populate local textures and global images. This method naturally accomplishes images of objects with familiar and highly specific structures compared to patch-based methods. [PERSON] et al. [31]extended the GAN approach by integrating an attention mechanism and proposed a prediction method based on deep generative model applicable to synthesize novel image structures. [PERSON] et al. [32] introduced local convolution during the image restoration and convolved encoded, low-level features into a globally predicted texture. This method was used to arbitrarily restore non-centered and irregularly defective areas. [PERSON] et al. [7] proposed an edge information-based restoration method to restore image details. There were two parts of the model, structural prediction and image completion.  
The development of deep learning techniques in the field of image restoration is rapid; however, artifact restoration still requires further exploration. Current research has been unable to show the restoration process in defective areas and lacks feedback on the restoration results. An online work platform is necessary for human-computer collaboration, and the selection and judgment of professionals in the workflow should be incorporated. This would help the restoration model be continuously improved in terms of the accuracy of the restoration results and establish an intelligent work platform for the restoration of Dunhuang wall murals.