Intelligent Restoration Technology of Mural Digital Image Based on Machine Learning Algorithm

Abstract

Influenced by the natural environment and preservation conditions, the frescoes in the monastery were damaged to some extent. In order to solve the fresco image restoration conversion and structural decomposition defects, an intelligent fresco digital image restoration technique based on machine learning algorithm is proposed. Firstly, the digital image information of mural is collected by a scanning program, and the data in the database is extracted. The mean filter template is used to restore the color of mural digital image, and the Gaussian template is used to restore the color of image details. The two are superimposed to ensure that the image is not affected by distortion and halo, so as to ensure the clarity of image and boundary. The depth learning model of local fuzzy feature restoration of mural digital image is obtained by using multimodal feature decomposition method, and the intelligent restoration of mural digital image is realized by this model. The experimental results show that this method can effectively repair the local fuzzy features of mural digital image. The repair precision is more than 95.7% and fills in the missing information from the image, and the image quality is good.