Abstract
Considering the problems of fuzzy repair and low pixel similarity matching in the repair of existing tomb murals, we propose a novel tomb mural repair algorithm based on sequential similarity detection in this paper. First, we determine the gradient value of tomb mural through second-order Gaussian Laplace operator in LOG edge detection and then reduce the noise in the edge of tomb mural to process a smooth edge of tomb mural. Further, we set the mathematical model to obtain the edge features of tomb murals. To calculate the average gray level of foreground and background under a specific threshold, we use the maximum interclass variance method, which considers the influence of small cracks on the edge of tomb murals and separates the cracks through a connected domain labelling algorithm and open and close operations to complete the edge threshold segmentation. In addition, we use the priority calculation function to determine the damaged tomb mural area, calculate the gradient factor of edge information, obtain the information entropy of different angles, determine the priority of tomb mural image repair, detect the similarity of tomb mural repair pixels with the help of sequential similarity, and complete the tomb mural repair. Experimental results show that our model can effectively repair the edges of the tomb murals and can achieve a high pixel similarity matching degree.
Highlights
Ancient murals, as one of China’s most important cultural assets, offer a wealth of historical, cultural, and aesthetic information.ey serve as an important carrier for China’s 5000-year civilisation [1]. e uncovered murals contain several ailments, such as fractures, fading, armoring, and other maladies because of natural weathering and man-made damage. e employment of digital image restoration technologies to help in the conservation of historical murals can minimize the difficulties of protecting ancient murals and the degradation caused by human causes
We propose a tomb mural restoration algorithm based on sequential similarity detection to cope with the aforementioned problems
By determining the gradient value of the tomb mural through the second-order Gaussian Laplace operator in LOG edge detection, we reduce the noise in the edge of the tomb mural and enhance the smoothness of the edge of the tomb mural
Summary
As one of China’s most important cultural assets, offer a wealth of historical, cultural, and aesthetic information. We can minimize the noise in the edge of tomb paintings and achieve the smoothness of the edge of tomb murals by calculating the gradient value of tomb murals and utilizing the second-order Gaussian Laplace operator in LOG edge detection [9] in the edge feature extraction of tomb murals It creates a mathematical model of tomb mural features to obtain tomb mural edge features. To finish the edge threshold segmentation of tomb paintings, a linked domain labelling algorithm and open and close operations are used to separate the cracks, taking into account the effect of tiny fractures on the edge of the tomb murals [11]. Where K[P(S)] represents the real value tomb chamber mural image with mean zero, U(X) represents the probability density function, and (C4(s)/[I(i, j)]2) represents the average zero value of the restored tomb room murals [14]
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