RETRACTED: Digital object restoration using generalized regression neural network deep learning—Taking Dunhuang mural restoration as an example

Abstract

In this research, Dunhuang murals is taken as the object of restoration, and the role of digital repair combined with deep learning algorithm in mural restoration is explored. First, the image restoration technology is described, as well as its advantages and disadvantages are analyzed. Second, the deep learning algorithm based on artificial neural network is described and analyzed. Finally, the deep learning algorithm is integrated into the digital repair technology, and a mural restoration method based on the generalized regression neural network is proposed. The morphological expansion method and anisotropic diffusion method are used to preprocess the image. The MATLAB software is used for the simulation analysis and evaluation of the image restoration effect. The results show that in the restoration of the original image, the accuracy of the digital image restoration technology is not high. The nontexture restoration technology is not applicable in the repair of large-scale texture areas. The predicted value of the mural restoration effect based on the generalized neural network is closer to the true value. The anisotropic diffusion method has a significant effect on the processing of image noise. In the image similarity rate, the different number of training samples and smoothing parameters are compared and analyzed. It is found that when the value of δ is small, the number of training samples should be increased to improve the accuracy of the prediction value. If the number of training samples is small, a larger value of δ is needed to get a better prediction effect, and the best restoration effect is obtained for the restored image. Through this study, it is found that this study has a good effect on the restoration model of Dunhuang murals. It provides experimental reference for the restoration of later murals.

RETRACTED: User acceptance of machine learning models – Integrating several important external variables with technology acceptance model

“…Simple and interpretable functions do not make the most accurate predictions. 11 In the pursuit of high-precision results, many MLM are black boxes. But in some industries with a strict regulatory regime, the MLM must be transparent and interpretable.…”

Constructing a mobile visual search framework for Dunhuang murals based on fine-tuned CNN and ontology semantic distance

“…The Dunhuang murals are of great research value, especially for the study of religion, class relations, costumes, architecture and humanistic tales from different periods (such as Zhang Qian's Diplomatic Missions [张骞出使西域]) (Hu, 1993). In past research studies, taking the Dunhuang murals as objects of restoration, the role of digital repair combined with deep learning algorithms in the mural restoration was explored (She, 2020), and the line-drawing enhanced interactive system for the Dunhuang mural restoration was built (Fu et al, 2017). An improved inpainting algorithm for repairing the Dunhuang murals was proposed, and it obtained good visual effects, as well as improved objective evaluation EL 40,3 values, such as the peak signal-to-noise ratio of the image (Chen et al, 2020a(Chen et al, , 2020b.…”

Unveiling Patterns and Colors in Architectural Paintings: An Analysis by K-Means++ Clustering and Color Ratio Analysis

“…Chinese cultural relics represent the tangible legacy of the Chinese national spirit, while subjectively they symbolize their exceptional national spirit. The preservation and restoration of cultural relics contribute not only to gaining relevant insights from history [5,6] but also to strengthening the sense of spiritual self-confidence and cultural unity in China [7].…”