# Feature Separation and Fusion to Optimise the Migration Model of Mural Painting Style in Toms
## 3 Method
### Style Feature Separation and Fusion  
In order to synthesise mural images from tomb murals in a targeted manner, this paper intends to distinguish between low- and high-level detail features depicted in the murals. The low-level features pertain to personal identification information and depict the figure's identity status within the tomb mural. There exists a notable correlation among diverse features found within the images depicted on tomb murals. This correlation further complicates the process of synthesizing the character's identity both qualitatively and quantitatively. However, the feature-decoupling technique serves as an effective measure to neutralise the impact of this correlation upon expression synthesis. This research aims to synthesise low-level images of tomb murals or to locate and quantitatively control fine high-level features in order to decouple the intertwined data variables in the original data space. Despite the presence of multiple features in the entire image, conventional generative adversarial networks are restricted to generating the entire image based on a 512-dimensional feature potential code. As it is infeasible to depict the entire 512-dimensional vector, the feature vectors are prone to entanglement. If each characteristic is assigned to one dimension of the vector, the other attributes can only be regulated conjointly by merging several dimensions. This largely affects the feature stability for the fine control of expression synthesis, which cannot be achieved with a single feature. Therefore, it is argued in this paper that the autonomous and precise control of the features of tomb murals can be achieved using feature decoupling. In this paper, we use the mapping network of the StyleGAN2 generator for feature separation. This means that a mapping network consisting of eight fully connected layers is used. The mapping network converts the input feature codes into intermediate vectors that have the same form as the input vectors. The intermediate vectors are then fed into the StyleGAN2 18-layer generative network, which generates 18 control vectors after passing through the generative network. The vectors produced by the trained generative model do not need to follow the distribution of the training data because the inputs to the generator are encoded intermediate vectors,  
Figure 3: Algorithmic network structure of this paper. thus reducing the correlation between features. Figure 4 shows the structural framework of the implementation. In this paper, the advantage of ResNet50 [44] is sufficient network depth to extract more feature information in different layers. Therefore, we implement feature code extraction of real images using ResNet50 to optimise the start vector in the feature separation framework [45]. We feed the features extracted from ResNet50 into the generator for image generation and adjusted the feature code for each level of image generation to match the image at that level. The chosen generator has nine levels, and each level has two levels of feature codes to control the direction of image synthesis. Thus, after a complete run of the generator, 18 \(\times\) 512-dimensional feature latent codes can be obtained. In the whole feature separation framework, the Euclidean distance between the original image features and the generated image features is calculated as a loss function, and then the features of the original and generated images [46] are extracted using the VGG19 [1] network. Next, the final tuned feature code is used as input to the generator, and model optimisation is performed based on the Adam optimiser until the loss value reaches the preset value. At this point, the feature latent codes used in each stage of generation are considered ideal latent codes, and the generated image obtained by using the feature codes as inputs is considered the optimal image.