Ancient restorations at Hierapolis of Phrygia (Denizli, Turkey): Interdisciplinary research on materials and technologies

Andreotti, Alessia; Baroni, Sandro; Bonaduce, Ilaria; Bozza, Sara; Cantisani, Emma; Ismaelli, Tommaso; Vettori, Serena

doi:10.1016/j.jasrep.2018.09.005

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Moreover, with the influence of man-made factors and natural disasters, its damage is inevitable, so it must be protected. 7 At present, the damaged murals are usually repaired based on textured and nontextured images. In the technology of texture restoration, texture synthesis is often used, which uses sample pixels or blocks to match and fill unknown areas, or decomposes the image into structures and textures to repair different levels of the image carefully.…”

Section: Introductionmentioning

confidence: 99%

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

She

2020

International Journal of Electrical Engineering & Education

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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.

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Section: Introductionmentioning

confidence: 99%

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

She

2020

International Journal of Electrical Engineering & Education

View full text Add to dashboard Cite

show abstract

Ancient restorations at Hierapolis of Phrygia (Denizli, Turkey): Interdisciplinary research on materials and technologies

Cited by 1 publication

References 8 publications

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

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

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