Spectral-domain optical coherence tomography for the non-invasive investigation of the pigment layers of Tang Dynasty tomb murals exhibited in museums

Tong, Ruizhan; Hu, Manli; Liu, Xiaobo; Zhang, Qunxi; Ge, Haihong; Gang, Tingting; X, Bai; Zuo, Chi; Bian, Ce

doi:10.1016/j.ijleo.2019.163311

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…Moreover, since the establishment of the Dunhuang Research Institute in 1950, the protection and restoration of Dunhuang cultural relics has officially begun. 6 After decades, cultural relics restorers have restored murals with an area of more than 4000 m 2 . However, with the passage of time, the falling off of walls and so on is not conducive to the long-term preservation of murals.…”

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

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“…At the early stage of OCT technology, it was widely used in the field of ophthalmology [18][19][20]. However, both the rapid improvement of the OCT principle of operation and the development of hardware manufacturing technology have provided the OCT systems with significant improvement in terms of their performance, and their application fields quickly expanded; they are now being used in areas such as the preservation of cultural heritage, criminal investigation, the pharmaceutical industry, and nondestructive testing for composite structures [20][21][22][23][24][25]. In all these applications, researchers have shown more concerns about the availability of NDT for composite materials and encouraged the development of more NDT systems to ensure that composite materials are tested adequately with the right NDT tools [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Thickness Measurement of Self-Lubricating Fabric Liner of Inner Ring of Sliding Bearings Using Spectral-Domain Optical Coherence Tomography

et al. 2023

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This study presents a novel and highly accurate method of measuring the geometric thickness of the self-lubricating fabric liner of bearings by combining the optical coherence tomography (OCT) technology and the Hanning-windowed energy centrobaric method (HnWECM). The geometric thickness of wear-resistant coating material is one of the important indicators for evaluating its wear, and the measurement of its geometric thickness is of great significance for preventing coating failure. To address the issue of significant measurement errors caused by using the refractive index of the sample instead of the group refractive index to calculate the material’s geometrical thickness in previous OCT research and applications, our proposed method can accurately measure the geometrical thickness of materials without the influence of the refractive index of the material. Moreover, this method exhibits the advantages of non-contact and high precision, since it utilizes an SD-OCT system, making it a novel method for extracting the physical parameters of composite materials. The geometric thickness of the peeled-off liner obtained from our method is compared with the thickness measured by the spiral micrometer to evaluate its accuracy. The experimental results indicate that the thickness measured by the spiral micrometer was 172 μm, while the maximum difference in the data obtained by our method was 171.261 μm. This suggests that the difference between the two methods is less than 0.430%, which verifies the accuracy and validity of our method. Additionally, the obtained geometric thickness and the optical thickness of the peeled-off liner are used to evaluate the group refractive index of this material. The inside geometrical structure of the self-lubricating fabric liner on the end face and inner ring of the sliding bearing is imaged with this group refractive index. The measurement of the inner ring liner of the sliding bearing proves the flexibility of the fiber-optic OCT and provides a non-contact, nondestructive testing method for measuring the geometric thickness and internal geometric structure of composite materials.

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“…Women's garments in this period were rich and gorgeous, strange and complex, forming was the most wonderful chapter in Chinese garments [2]. Tang Dynasty tomb murals with a unique style of architecture, simple and vivid characters, distinctive utensils, a simple and lively landscape, and lifelike animals and plants, depict the etiquette norms, life customs, costume characteristics, entertainment and architectural style at that time, which is an important image material for the study of social life in Tang Dynasty, especially the noble life and spiritual pursuits [3]. At present, some murals of Tang tombs fall off, and the color degeneration is serious.…”

Section: Introductionmentioning

confidence: 99%

Archaeology and Restoration of Costumes in Tang Tomb Murals Based on Reverse Engineering and Human-Computer Interaction Technology

Liu

et al. 2022

Sustainability

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This paper takes the tomb murals as the research object, and realizes the development of the costume patterns of the Tang tomb murals and the 3D simulation restoration of the costumes through 3D interactive clothing pattern-making technology and virtual simulation technology. Firstly, the 3D garment model is constructed in the virtual environment according to the costume outline of the Tang Dynasty tomb mural costume. Then, the structural curves of the garment are drawn on the 3D garment according to the characteristics of the Tang Dynasty tomb mural garment style, the 3D surface is expanded and surrounded by these curves into the 2D garment plane, and the expanded surface is adjusted to obtain the 2D garment plane pattern. We use 3D virtual simulation technology to sew the patterns of Tang Dynasty tomb mural costumes and realize the virtual simulation restoration of Tang Dynasty tomb mural costumes. Finally, we create a fuzzy comprehensive evaluation of the restoration effect of the restored costumes. Compared with the traditional costume restoration methods, the method proposed in this paper reduces the technical requirements for operators in the restoration process without destroying cultural relics, and provides a new method for the rapid simulation and restoration of ancient Chinese costumes.

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Spectral-domain optical coherence tomography for the non-invasive investigation of the pigment layers of Tang Dynasty tomb murals exhibited in museums

Cited by 9 publications

References 12 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

Thickness Measurement of Self-Lubricating Fabric Liner of Inner Ring of Sliding Bearings Using Spectral-Domain Optical Coherence Tomography

Archaeology and Restoration of Costumes in Tang Tomb Murals Based on Reverse Engineering and Human-Computer Interaction Technology

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