# Feature Separation and Fusion to Optimise the Migration Model of Mural Painting Style in Toms
## 1 Introduction  
Tomb murals are a form of wall painting known for their long history, diversity of subject matter and wide-ranging artistic significance [1] These murals are a valuable repository of shared artistic and cultural heritage across the country and around the world. However, disseminating mural art presents more challenges than other forms of painting, such as oil painting and drawing. This is due to the unique nature of murals and the difficulties involved in completing the painting process. As shown in Figure 1, the tomb mural location is poorly lit. The murals are on both sides of the tomb passage, which is susceptible to damage by cuts and scrapes. The murals are usually of great length and need to be captured and photographed in batches of sub-lenses. Due to various factors, including time, each mural may differ. However, modern technology has enabled the restoration of tomb murals for public enjoyment. The restoration and digital display of the murals have used digital inpainting and stylistic relocation to preserve their integrity. For preserving tomb murals, digital inpainting is an effective approach. The restoration of fragmentary samples of tomb murals via the internet can reduce the cost of manual restoration and can prevent potential restoration errors. Recently, [PERSON] et al. implemented mural restoration using a deep learning network with added dual attention [2]. Figure 2 shows the restored mural split-screen of the tomb. Nonetheless, mural restoration algorithms necessitate numerous training samples as a foundation. The application of style migration algorithms for training sample provision has become a prevalent method. Specifically, mural style migration is considered beneficial for augmenting the dataset used for digital inpainting of tomb murals. This method provides a multitude of samples with distinct styles and textures, thus amplifying the training dataset of the restoration model. The restoration model can achieve a more comprehensive and robust feature representation, leading to improved quality and accuracy in the restoration outcomes. In addition, the mural-style migration samples emulate the damage conditions on the tomb murals, including cracks, fading, and missing elements. Consequently, the restoration model can accurately recognise damage patterns and analyse the damage to the murals. This facilitates a more precise restoration process. Finally, the images resulting from mural style migration can serve as evaluation criteria for restoration outcomes. The quality and accuracy of the restoration are measured quantitatively by comparing the similarity and consistency between the restored outcomes and the generated samples. The use of this assessment technique can provide quantitative metrics to help evaluate the performance and potential for improvement of the restoration model. In recent years, deep learning techniques have been continuously used by researchers for style migration experiments, such as [PERSON], who successfully performed style migration [3] on ordinary photographs by separating and reorganising the extraction of image style and content by convolutional neural networks, respectively. However, the application of this technique to tomb murals resulted in chaotic image colour fills and imbalanced image gaps, which could not solve the problem of large area restoration during image restoration. The dominant colour is extracted from the input image and converted into palette-quantised chroma and luminance channels [4]; contour shapes are extracted by the Laplacian of Gaussians filtering (LoG, LoG); the tensor quantisation is optimised to  
Figure 1: Diagram of the excavation site and layout of the mural in the tombs. (**a**) Tomb mural scene. (**b**) The location of the mural in the tomb. generate the image colour [5]; the contours are adaptively smoothed to generate the texture by flow field computation; and finally the colour images and drawn textures are mixed to produce the final result [6].