Ancient Murals Research Articles

PRN: progressive reasoning network and its image completion applications

Ancient murals embody profound historical, cultural, scientific, and artistic values, yet many are afflicted with challenges such as pigment shedding or missing parts. While deep learning-based completion techniques have yielded remarkable results in restoring natural images, their application to damaged murals has been unsatisfactory due to data shifts and limited modeling efficacy. This paper proposes a novel progressive reasoning network designed specifically for mural image completion, inspired by the mural painting process. The proposed network comprises three key modules: a luminance reasoning module, a sketch reasoning module, and a color fusion module. The first two modules are based on the double-codec framework, designed to infer missing areas’ luminance and sketch information. The final module then utilizes a paired-associate learning approach to reconstruct the color image. This network utilizes two parallel, complementary pathways to estimate the luminance and sketch maps of a damaged mural. Subsequently, these two maps are combined to synthesize a complete color image. Experimental results indicate that the proposed network excels in restoring clearer structures and more vivid colors, surpassing current state-of-the-art methods in both quantitative and qualitative assessments for repairing damaged images. Our code and results will be publicly accessible at https://github.com/albestobe/PRN.

Bringing the queen mother of the west to life: Digital reconstruction and analysis of Taoist Celestial Beings Worshiping mural’s apparel

Abstract Painted during the Yuan Dynasty, Taoist Celestial Beings Worshiping is a Taoist propaganda painting. It is one of the largest surviving ancient murals worldwide, featuring a variety of images of these beings dressed in costumes that vividly illustrate the blend of religious and artistic values in Chinese culture. To record and present the mural more intuitively and improve the study of Taoist Celestial Beings Worshiping, this article takes the example of five goddesses centered Queen Mother of the West in the mural and analyzes the styles, structures, colors, and patterns of the costumes from the perspective of costume engineering. Human models are established and the costumes at multiple levels are reconstruction by means of 3D virtual simulation technology. The display images are accompanied by QR codes, which can be scanned to view the 3D model. Finally, the Fuzzy Analytic Hierarchy Process was used to comprehensively evaluate the reconstruction effect of clothing, and the result was “good.” The resulting digital figure can realize the “revitalization of cultural relics” and provides a new perspective for the digital exhibition of murals, which is conducive to the development of digital tourism and promotes the development of traditional culture.

A text guided cross modal joint inpainting algorithm for ancient murals

Existing deep learning algorithms for ancient mural inpainting only consider prior information of mural images to guide the inpainting of damaged areas, lacking guidance from textual semantic information, which often leads to problems such as inconsistent semantics and disordered textures in the inpainting results. In this work, we propose a text guided cross modal joint inpainting algorithm for ancient murals. Firstly, a cross modal joint inpainting network for ancient murals was constructed, which introduced text semantic information as the control guidance for mural inpainting, providing contextual information for mural image inpainting and guiding the inpainting of damaged areas. Then, design a text encoder and an image encoder to extract text and image features respectively, including word and sentence features. Input the sentence features into the conditional enhancement module, resample the text sentences, and solve the problem of text sparsity. Secondly, design a text decoupling module that utilizes a multimodal attention mechanism to extract the semantic information of the missing parts of damaged murals, and design a text semantic enhancement module to improve the semantic representation ability of the text, achieve the repair of local details of murals, and solve the problem of texture disorder. Finally, the reconstruction and repair of murals are completed through a residual discriminator game and a spectral normalized Markov dual discriminator against the mural generation network. Through inpainting experiments on real Dunhuang murals, it has been shown that the proposed method is more effective than comparative methods in repairing damaged murals, achieving better visual perception and coordination in reconstruction, and outperforming comparative algorithms in both subjective and objective evaluations.

Automatic labeling framework for paint loss disease of ancient murals based on hyperspectral image classification and segmentation

Ancient murals have suffered from continuous damage over time, and especially paint loss disease. Therefore, disease labeling, as the basis for ancient mural restoration, plays an important role in the protection of cultural relics. The predominant method of disease labeling is currently manual labeling, which is highly dependent on expert experience, time consuming, inefficient and results in inconsistent accuracy of the marking effect. In this paper, we propose a labeling framework for paint loss disease of ancient murals based on hyperspectral image classification and segmentation. The proposed framework involves first the extraction of features from the hyperspectral image, and then image segmentation is performed based on the spatial features to obtain more accurate region boundaries. Then, the hyperspectral image’s regions are classified based on their spatial-spectral characteristics, and the candidate areas of paint loss disease are obtained. Finally, by leveraging the true color image segmentation results, the proposed disease labeling strategy combines the results of classification and segmentation to propose the final paint loss disease labeling areas. The experimental results show that the proposed method can not only combine the hyperspectral space and spectral information effectively to obtain accurate labeling of paint loss disease, but can also mark the paint loss disease not easily observed using ordinary digital cameras. Compared with the state-of-the-art methods, the proposed framework could be promising for accurate and effective paint loss disease labeling for ancient murals.

EMERGING TRENDS IN CHUARCHITRARANGAL - TRADITIONAL MURAL ART OF KERALA

The south Indian state of Kerala is home to charismatic murals painted between 9th and 18thcentury CE and are proudly known as Chuarchitrarangal. These paintings adorn the walls of Shrikovil – the sanctum sanctorum of Hindu temples all over Kerala that narrates stories of God and Goddesses. Kerala murals have largest collection of secco, based on ancient Indian mythological stories.This study aims to identify, analyse and document the emerging trends in Kerala Mural Paintings. Also, it will make an effort to forecast the future trends. Primary data of renowned artists and secondary data of ancient murals will be referred for the study. 7 temples which hold the best condition murals will be included in the research. The medieval era (9th to 12th century CE) saw the height of this art. The entry of European art into academia in the 19th century, hampered the local art and Kerala murals began to face a significant decrease. Because of the realistic finish of European painting and the use of new convenient oil paints, local traditional art styles were no longer recognized and respected. The Kerala mural tradition had already faded into history by the end of the 1900s. Currently Kerala has a decent number of professional muralists who work extensively in conventional and contemporary methods. This art is being revived steadily. New generation artists are experimenting to keep up with the pace and preferences of new world. Getting inspired from various genres, creating novel styles yet keeping the conventional value intact has become a major trend in Kerala mural art.

Assessing the carrying capacity for the environmental protection of the ancient artworks of India’s Ajanta caves

Purpose The purpose of this study is to investigate environmental factors impacting Ajanta mural deterioration, assessing global tourism effects and visitor conduct on cave environment and murals. This study recognizes stakeholder roles in conservation, providing data-driven insights to guide institutions like the Archaeological Survey of India. The objective is sustainable tourism practices to balance public access with mural preservation for future generations. Design/methodology/approach Over 25 years, Ajanta Caves' visitors doubled, impacting microclimatic conditions for ancient murals. This study assesses visitor impact to establish a regime and determine carrying capacity, considering temperature, humidity and pollution. Challenges arise from quantifying capacity because of variables. This research informs global tourism and heritage conservation, offering methodologies applicable to cultural sites worldwide. Findings This study examines environmental impacts on mural preservation in Ajanta Caves, including humidity, microbial growth, sunlight exposure, air quality and tourist presence. Tourist influx escalates CO2 levels, directly endangering murals. Concerns about particulate matter, especially during visits, emphasize the need for data-driven decision-making and modern technology use to protect Ajanta Caves' artwork, crucial because of its global significance and tourism-related vulnerabilities. Social implications This study carries substantial social implications with a global resonance. The active engagement of the local community and tourism stakeholders in conserving and promoting the Ajanta Caves fosters empowerment, igniting a sense of pride, ownership and responsibility among residents and ensuring sustainable enjoyment of cultural heritage while safeguarding it for future generations. In addition, there will be socioeconomic benefits to local residents such as employment opportunities as tour operators, tour guides, hospitality staff, artisans and souvenir shopkeepers. Originality/value This study integrates art conservation, environmental science, cultural heritage preservation and social aspects to address global tourism challenges. Focusing on a site of worldwide significance, this study offers practical strategies for artwork preservation, tourism management and environmental concerns. These recommendations provide real-world solutions applicable to heritage sites globally, bridging scientific analysis with social and cultural insights.

A New Method for Extracting Refined Sketches of Ancient Murals.

Mural paintings, as the main components of painted cultural relics, have essential research value and historical significance. Due to their age, murals are easily damaged. Obtaining intact sketches is the first step in the conservation and restoration of murals. However, sketch extraction often suffers from problems such as loss of details, too thick lines, or noise interference. To overcome these problems, a mural sketch extraction method based on image enhancement and edge detection is proposed. The experiments utilize Contrast Limited Adaptive Histogram Equalization (CLAHE) and bilateral filtering to enhance the mural images. This can enhance the edge features while suppressing the noise generated by over-enhancement. Finally, we extract the refined sketch of the mural using the Laplacian Edge with fine noise remover (FNR). The experimental results show that this method is superior to other methods in terms of visual effect and related indexes, and it can extract the complex line regions of the mural.

Dunhuang murals image restoration method based on generative adversarial network

AbstractMurals are an important part of China’s cultural heritage. After more than a 1000 years of exposure to the sun and wind, most of these ancient murals have become mottled, with damage such as cracking, mold, and even large-scale detachment. It is an urgent work to restore these damaged murals. The technique of digital restoration of mural images refers to the reconstruction of structures and textures to virtually fill in the damaged areas of the image. Existing digital restoration methods have the problems of incomplete restoration and distortion of local details. In this paper, we propose a generative adversarial network model combining a parallel dual convolutional feature extraction depth generator and a ternary heterogeneous joint discriminator. The generator network is designed with the mechanism of parallel extraction of image features by vanilla convolution and dilated convolution, capturing multi-scale features simultaneously, and reasonable parameter settings reduce the loss of image information. A pixel-level discriminator is proposed to identify the pixel-level defects of the captured image, and its joint global discriminator and local discriminator discriminate the generated image at different levels and granularities. In this paper, we create the Dunhuang murals dataset and validate our method on this dataset, and the experimental results show that the method of this paper has an overall improvement in the evaluation metrics of PSNR and SSIM compared with the comparative methods. The restored resultant image is more in line with the subjective vision of human beings, which achieves the effective restoration of mural images.

A virtual restoration network of ancient murals via global–local feature extraction and structural information guidance

Ancient murals are precious cultural heritages. They suffer from various damages due to man-made destruction and long-time exposure to the environment. It is urgent to protect and restore the damaged ancient murals. Virtual restoration of ancient murals aims to fill damaged mural regions by using modern computer techniques. Most existing restoration approaches fail to fill the loss mural regions with rich details and complex structures. In this paper, we propose a virtual restoration network of ancient murals based on global–local feature extraction and structural information guidance (GLSI). The proposed network consists of two major sub-networks: the structural information generator (SIG) and the image content generator (ICG). In the first sub-network, SIG can predict the structural information and the coarse contents for the missing mural regions. In the second sub-network, ICG utilizes the predicted structural information and the coarse contents to generate the refined image contents for the missing mural regions. Moreover, we design an innovative BranchBlock module that can effectively extract and integrate the local and global features. We introduce a Fast Fourier Convolution (FFC) to improve the color restoration for the missing mural regions. We conduct experiments over simulated and real damaged murals. Experimental results show that our proposed method outperforms other three comparative state-of-the-art approaches in terms of structural continuity, color harmony and visual rationality of the restored mural images. In addition, the mural restoration results of our method can achieve comparatively high quantitative evaluation metrics.

EXTRACTION OF PAINT LOSS IN ANCIENT MURALS BASED ON 3D RESIDUAL NEURAL NETWORK

Abstract. The ancient murals of Qutan Temple in Qinghai Province have a very serious loss of paint. Moreover, the main components of the base color paint layer in the paint loss area and the white patterns in the murals are both calcified, which are similar in color and spectral features. Thus, it is difficult to distinguish them by only using spectral features. A method of paint loss area extraction based on 3D residual network with multi-scale feature fusion is proposed. Firstly, the hyperspectral images with paint loss regions were collected by hyperspectral images. They are pre-processed to establish the training data set. Secondly, 3D residual network models are constructed using 3×3×3, 3×3×5 and 5×5×3 convolution kernels to realize the extraction and fusion of spatial and spectral features at different scales of hyperspectral images. The produced mural hyperspectral dataset is used for network training to obtain the prediction model. Finally, the hyperspectral images are input into the trained model to achieve the extraction of paint loss. After comparing different methods, the experimental result shows that the proposed method can improve the extraction accuracy of mural paint loss and serve as a reference for other deteriorations extraction.

Research on the Reinforcement and Inhibition of Water–Salt Activity in Mural Ground Layers by Superhydrophobic SiO2 Particles

Due to notable water–salt activities, salt damage easily recurs and becomes one of the biggest challenges for the protection of ancient murals. Herein, superhydrophobic SiO2 materials with different sizes were used to modify mural ground layer substrates, and the improvement effect mechanisms were systematically evaluated with scanning electron microscopy (SEM), X-ray diffraction (XRD), laser scanning confocal microscopy (LSCM), and a contact angle instrument. The results show that the superhydrophobic SiO2 can spread into the substrates though holes and cracks and further increase the contact angles of the substrates to water droplets. Compared with the initial ground layer substrate, the substrates treated with the superhydrophobic SiO2 possess stronger mechanical strength and a better ability in suppressing water–salt activity. In particular, larger-size SiO2 (mSiO2) maintains better mechanical reinforcement in the substrates, because mSiO2 can provide better support in the internal gaps of the substrates. By contrast, nSiO2 can spread deeper into the substrate than mSiO2, and more greatly improve the contact angle to water droplets, endowing nSiO2 with a better ability to restrain water–salt activity. Our study provides an alternative idea for solving salt damage in murals, and promotes the application of SiO2 materials in heritage conservation.

Dunhuang Dreamscape: A Study and Exploration of Dunhuang Art in Modern Interior Design based on Virtual Reality Technology

In the context of the national initiative of the "Belt and Road" policy and the vigorous promotion of cultural soft power, interior design products infused with cultural creativity have emerged as significant carriers of cultural development and heritage. This paper explores the innovative application of virtual reality technology in the field of interior design, with a special focus on the integration of Dunhuang elements in design practices. By combining virtual reality technology with Dunhuang art, a novel paradigm for interior design is proposed, blending ancient murals, patterns, and colors with modern computer graphics technology, infusing spaces with a unique historical charm and cultural significance. From the perspectives of virtual reality technology, inheritance of Dunhuang artistic elements, Dunhuang-style spatial design based on virtual reality, user experience, and emotional resonance, this paper delves into an in-depth exploration. Its aim is to expand innovative paths within interior design, while simultaneously providing new ideas and methods for cultural heritage and spatial perception domains.

Ancient mural inpainting via structure information guided two-branch model

Ancient murals are important cultural heritages for our exploration of ancient civilizations and are of great research value. Due to long-time exposure to the environment, ancient murals often suffer from damage (deterioration) such as cracks, scratches, corrosion, paint loss, and even large-region falling off. It is an urgent work to protect and restore these damaged ancient murals. Mural inpainting techniques refer to virtually filling the deteriorated regions by reconstructing the structure and texture elements of the mural images. Most existing mural inpainting approaches fail to fill loss contents that contain complex structures and diverse patterns since they neglect the importance of structure guidance. In this paper, we propose a structure-guided two-branch model based on the generative adversarial network (GAN) for ancient mural inpainting. In the proposed model, the mural inpainting process can be divided into two stages: structure reconstruction and content restoration. These two stages are conducted by using a structure reconstruction network (SRN) and a content restoration network (CRN), respectively. In the structure reconstruction stage, SRN employs the Gated Convolution and the Fast Fourier Convolution (FFC) residual block to reconstruct the missing structures of the damaged murals. In the content restoration stage, CRN uses the structures (generated by SRN) to guide the missing content restoration of the murals. We design a two-branch parallel encoder to improve the texture and color restoration quality for the missing regions of the murals. Moreover, we propose a cascade attention module that can capture long-term relevance information in the deep features. It helps to alleviate the texture-blur and color-bias problem. We conduct experiments on both simulated and real damaged murals, and compare our inpainting results with other four competitive approaches. Experimental results show that our proposed model outperforms other approaches in terms of texture clarity, color consistency and structural continuity of the restored mural images. In addition, the mural inpainting results of our model can achieve comparatively high quantitative evaluation metrics.

Metagenomic analysis reveals taxonomic and functional diversity of microbial communities on the deteriorated wall paintings of Qinling Tomb in the Southern Tang Dynasty, China

The microbial colonization on ancient murals attracts more and more attention since the threaten by microorganisms was first reported in Lascaux, Spain. However, the biodeterioration or biodegradation of mural paintings resulted by microorganisms is not clear yet. Especially the biological function of microbial communities in different conditions remained largely unaddressed. The two mausoleums of the Southern Tang Dynasty are the largest group of emperor mausoleums during the Five Dynasties and Ten Kingdoms period in China, which are of great significance to the study of the architecture, imperial mausoleum systems and art in the Tang and Song Dynasties. To make clear the species composition and metabolic functions of different microbial communities (MID and BK), we analyzed the samples from the wall paintings in one of the two mausoleums of the Southern Tang Dynasty with metagenomics method. The result showed totally 55 phyla and 1729 genera were detected in the mural paintings. The two microbial community structure were similar with the dominance of Proteobacteria, Actinobacteria and Cyanobacteria. However, the species abundance presented a significant difference between two communities at genus level --- MID is Lysobacter, Luteimonas are predominant in MID while Sphingomonas and Streptomyces are popular in BK, which is partially attributed to the different substrate materials of murals. As a result, the two communities presented the different metabolic patterns that MID community was mainly participated in the formation of biofilm as well as the degradation of exogenous pollutants while the BK was predominantly related to the photosynthesis process and biosynthesis of secondary metabolites. Taken together, these findings indicated the effect of environmental factor on the taxonomic composition and functional diversity of the microbial populations. The installation of artificial lighting needs to be considered carefully in the future protection of cultural relics.

A generative adversarial network model fused with a self‐attention mechanism for the super‐resolution reconstruction of ancient murals

AbstractThe problem that the texture details of low‐resolution (LR) digital images of ancient murals are ambiguous persists. To solve this problem, this study proposes a super‐resolution (SR) reconstruction method for fuzzy murals based on a generative adversarial network with self‐attention (SA). The network uses a blur kernel and realistic noise data to add blur and noise, respectively, to a high‐resolution (HR) image to obtain an original LR image. Then, a feature image with the same size as that of the input image is obtained through a SA module. Finally, the feature information extracted from the image is input into the high‐resolution image space by using a subpixel convolution layer to realize the image enlargement process from an LR to an HR. Experiments evaluate the proposed approach both objectively and subjectively. The objective evaluation results show that compared with other SR reconstruction algorithms, the proposed algorithm's peak signal‐to‐noise ratio (PSNR) is increased by 0.04 to 3.78 dB on average, and its structural similarity is increased by 0.002 to 0.191. A subjective perception evaluation shows that the developed algorithm can better reconstruct the texture details of murals, thus better meeting the visual perception needs of the public. The method proposed in this study can satisfactorily reconstruct the texture details of murals, which may provide technical guidance for the development of mural protection plans. Furthermore, it may be of certain practical significance for the SR reconstruction of ancient murals.

Application of hyperspectral imaging technology to digitally protect murals in the Qutan temple

Hyperspectral imaging technology is a research hotspot in the field of cultural heritage protection. It can be used to quickly and noninvasively obtain detailed spectral information from the surfaces of cultural relics of different categories. We can intuitively analyse pigment compositions, line characteristics, painting skills and patterns using spectral information. Hyperspectral imaging has high scientific significance and application value for the protection, restoration and research of ancient murals and other cultural relics. In this study, a mural from Daheitian hall in the Qutan temple, Qinghai Province, China, was used as a sample. The hyperspectral data were acquired and analysed for several purposes. Pigment spectral matching and abundance inversion were carried out to obtain the pigment distribution. These data were enhanced by continuum removal and histogram stretching to obtain hidden information. The dark channel prior, Criminisi and Retinex methods were used to virtually restore the image of the mural. The results indicated that by using hyperspectral imaging data, the constructed pure pigment spectrum library and suitable approaches, the types and distributions of mural pigments can be quantitatively analysed, and the lines in murals can be extracted. Hyperspectral images are helpful for identifying information hidden by pigments or surface materials. Mural images can be enhanced, and hidden information can be highlighted using enhancement methods, such as continuum removal and histogram linear stretching. In addition, hyperspectral imaging data have unique advantages in the restoration of mural images, and the combination of defogging methods and image inpainting algorithms can realize the virtual restoration of mural images. In brief, hyperspectral imaging technology was found to have a highly favourable effect on pigment analysis, line extraction, information enhancement, hidden information extraction and the virtual restoration of ancient murals.

Optimized method for segmentation of ancient mural images based on superpixel algorithm.

High-precision segmentation of ancient mural images is the foundation of their digital virtual restoration. However, the complexity of the color appearance of ancient murals makes it difficult to achieve high-precision segmentation when using traditional algorithms directly. To address the current challenges in ancient mural image segmentation, an optimized method based on a superpixel algorithm is proposed in this study. First, the simple linear iterative clustering (SLIC) algorithm is applied to the input mural images to obtain superpixels. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to cluster the superpixels to obtain the initial clustered images. Subsequently, a series of optimized strategies, including (1) merging the small noise superpixels, (2) segmenting and merging the large noise superpixels, (3) merging initial clusters based on color similarity and positional adjacency to obtain the merged regions, and (4) segmenting and merging the color-mixing noisy superpixels in each of the merged regions, are applied to the initial cluster images sequentially. Finally, the optimized segmentation results are obtained. The proposed method is tested and compared with existing methods based on simulated and real mural images. The results show that the proposed method is effective and outperforms the existing methods.

Evaluation of the effectiveness and compatibility of nanolime for the consolidation of earthen-based murals at Mogao Grottoes

• First time use nanolime for the conservation of Chinese earthen-based murals. • One treatment increases surface cohesion with acceptable color change. • Interaction mechanism between nanolime and pigments particles was investigated. • One treatment with nanolime decreases the pore diameter while keeps the porosity. Mogao Grottoes is a World Cultural Heritage site with splendid ancient murals and sculptures which record the culture and history of the Silk Road. To consolidate these earthen-based murals with multilayer structure, nanolime were synthesized with anion exchange method and the effectiveness and compatibility of using it for the conservation of Dunhuang murals were evaluated. For mural samples, the nanolime treatment increases the surface cohesion and hardness while changing the colors in an acceptable range, in the meantime decreases the pore diameter while keeping the porosity equivalent. To the best of our knowledge, this is the first time nanolime was used for the restoration of earthen-based murals.

TMCrack-Net: A U-Shaped Network with a Feature Pyramid and Transformer for Mural Crack Segmentation

The detection of crack information is very important in mural conservation. In practice, the number of ancient murals is scarce, and the difficulty of collecting digital information about murals leads to minimal data being collected. Crack information appears in pictures of paintings, which resembles painting traces and is easy to misidentify. However, the current mainstream semantic segmentation networks directly use the features of the backbone network for prediction, which do not fully use the features at different scales and ignore the differences between the decoder and encoder features. This paper proposes a new U-shaped convolutional neural network with feature pyramids and a transformer called TMCrack-net. Instead of U-Net’s jump-join, an AG-BiFPN network is used, which consists of two modules: a channel cross-fusion (CCT) module with a transformer and a bidirectional feature pyramid network. While fully using the information in different network dimensions, the channel cross-fusion module optimizes the final features of each layer to reduce the confounding effect caused by the fusion of features. For the fusion of multi-scale channel information with decoder features, we designed a fusion module based on a channel attention (called FCA) to guide the fusion of enhanced encoder features with decoder features and reduce the ambiguity between the two feature sets. To demonstrate the effectiveness and generalization of the model, TMCrack-Net was evaluated on the Tang Dynasty tomb chamber mural dataset and Crack500. MIou values of 0.7731 and 0.7944 were achieved, respectively, which are better than those of other advanced crack detection methods. The method yields accurate segmentation performance and is advantageous for mural painting crack segmentation tasks.

Automatic calibration of crack and flaking diseases in ancient temple murals

Many precious ancient murals are seriously deteriorated due to long-term environmental influences and man-made destructions. How to effectively protect ancient murals and restore these murals’ original appearance has become an urgent problem for field experts. Modern computer technology makes it possible to virtually restore the deteriorated areas in ancient murals. However, most existing mural restoration approaches require manual calibration of the deteriorated areas, which is very difficult and time-consuming. It has been noticed that the earth layer flaking and cracks are the most common problems of ancient temple murals. This paper proposes an automatic calibration method for the earth layer flaking and cracking deterioration of murals by taking temple murals from the Ming Dynasty in Zhilin Temple as the study object. First, we extract the texture and line features of the deteriorated murals by using multi-dimensional gradient detection in the HSV space. Then, a guided filter operation is employed to highlight the disease (deteriorated) areas and meanwhile suppress other unwanted areas, which helps to extract the flaked areas or cracked lines from the digital murals. The filtered images are segmented by using an automatic threshold to obtain the initial masks of the mural disease areas. Next, we use a two-dimensional tensor voting technique to connect the discontinuous edge curves of the extracted disease areas. Afterwards, the masks of flaking and cracking areas can be generated after morphological processing. Finally, we obtained the calibration results by adding the masks to the original digital murals. Experimental results show that our method can rapidly and accurately calibrate the cracks and the earth layer flaking diseases in the ancient murals. As compared to existing calibration approaches, our method can achieve better performance in subjective visual quality and objective evaluation metrics. Moreover, the method does not need human-computer interaction. This research work provides a solid foundation for the following virtual and practical restoration of ancient murals.