Virtual Restoration of Mural Color Based on Artificial Intelligence

Abstract

Abstract In recent years, the development of artificial intelligence can be described as rapid change, which provides a safer method for the virtual restoration of mural colors. This article mainly studies the virtual restoration of mural color based on artificial intelligence. This paper constructs the minimum spanning tree of the segmentation line and transforms it into a graph, finds out the closed sub-ring as the segmentation line from the graph, and uses the depth-first search algorithm to extract the surface in the segmentation line area. This paper performs threshold segmentation on the mural image to obtain the drop-off area. The threshold T is selected based on experience; the pixel points of the same attribute are merged by 8 adjacent methods, that is, the pixel value is 1, and then the closed operation is used to achieve the purpose of continuous drop-off information. In order to control the tendency of global optimization and neighborhood correlation, a proportional parameter needs to be added, and the parameters are adjusted to obtain a balance between neighborhood correlation and global optimization, and the best matching point can be obtained. The data shows that the average objective evaluation score of Criminisi algorithm is 0.794603, and the average objective evaluation score of the improved algorithm in this paper is 0.848665. The results show that artificial intelligence technology plays an extremely important role in the virtual restoration of mural colors.