Abstract
In this article, we study the mural restoration work based on artificial intelligence-assisted multiscale trace generation. Firstly, we convert the fresco images to colour space to obtain the luminance and chromaticity component images; then we process each component image to enhance the edges of the exfoliated region using high and low hat operations; then we construct a multistructure morphological filter to smooth the noise of the image. Finally, the fused mask image is fused with the original mural to obtain the final calibration result. The fresco is converted to HSV colour space, and chromaticity, saturation, and luminance features are introduced; then the confidence term and data term are used to determine the priority of shedding boundary points; then a new block matching criterion is defined, and the best matching block is obtained to replace the block to be repaired based on the structural similarity between the block to be repaired and the matching block by global search; finally, the restoration result is converted to RGB colour space to obtain the final restoration result. An improved generative adversarial network structure is proposed to address the shortcomings of the existing network structure in mural defect restoration, and the effectiveness of the improved modules of the network is verified. Compared with the existing mural restoration algorithms on the test data experimentally verified, the peak signal-to-noise ratio (PSNR) score is improved by 4% and the structural similarity (SSIM) score is improved by 2%.
Highlights
Computer graphics and computer vision have gradually come into the limelight as their performance has developed rapidly
Computer graphics and computer vision have become an integral part of computer development, and image segmentation, image enhancement, image recognition, and image restoration have always played an important role in these developments [1]
We introduce semantic segmentation based on an expanded convolutional neural network to guide the colour restoration of mural images to avoid the problems of mismatching colour contents of mural images and poor similarity of mural images and use a convolutional neural network to extract features of mural images and propose a mural image colour restoration method combining semantic segmentation and convolutional neural network to improve the accuracy of mural image colour restoration
Summary
Computer graphics and computer vision have gradually come into the limelight as their performance has developed rapidly. The convolutional neural network colour restoration method based on image feature similarity does not fall into an unreasonable local optimum solution due to the gradient disappearance problem, the colour content is often mismatched because of the complex structure of mural images [18]. E convolutional neural network-based image colour restoration method is to separate the content information and colour texture information in the image and perform colour restoration and texture synthesis, but because the drawing process of mural images is extremely tedious, their content information and colour information will not be separated. We use the maximum mean difference constraint to extract the global colour features of mural images and Markov random field constraint to extract the local features of mural images and propose a multiple-constrained convolutional neural network-based mural image colour virtual restoration method, which aims to extract the global colour information of mural images while preserving the local colour texture information of mural images. We introduce semantic segmentation based on an expanded convolutional neural network to guide the colour restoration of mural images to avoid the problems of mismatching colour contents of mural images and poor similarity of mural images and use a convolutional neural network to extract features of mural images and propose a mural image colour restoration method combining semantic segmentation and convolutional neural network to improve the accuracy of mural image colour restoration
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