Virtual Restoration of Cracks in Murals Based on Maximum Entropy Thresholding and Planar Structure Information

Ancient Chinese murals are true portrayals of ancient Chinese life, but well-preserved murals are rare. Therefore, ancient mural preservation and repair are critical. To address the poor superresolution reconstruction of mural images with unclear textures and fuzzy details, we developed an improved generative adversarial network (GAN) algorithm based on asymmetric pyramid modules for ancient mural superresolution reconstruction. Asymmetric pyramid modules, which are composed of a series of dense compression units, were used to learn image features. To analyze the reconstructed image features, a perceptual loss function was integrated to optimize the model performance. The use of the improved algorithm for low-resolution mural images increased the image resolution while preserving their original feature details and textures, and the improvement effect was visually observed in terms of indices such as the peak signal-to-noise ratio and structural similarity. Compared with other superresolution-related algorithms, the proposed model increased the peak signal-to-noise ratio by 0.20–6.66 dB. The GAN-based mural superresolution reconstruction algorithm proposed in this study effectively improved the performance of reconstructed high-resolution mural images, which increases the significance of the reconstructed image for future research.

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.

The author shows that mathematical morphology imaging filter techniques enhance the effectiveness and versatility of maximum entropy thresholding in separating foreground and background, especially for small targets. Mathematical morphological image processing techniques, specifically openings and closing, tend to set large areas of a gray- level image to the same gray-level while preserving the number of gray-levels present in small areas, i.e., small targets. In an entropic analysis of the image, this equates to minimizing the entropy of the areas set to identical gray-levels, while conversely enhancing that of small, information-rich regions. Maximum entropy thresholding entropy contribution of each gray-level. Thus, prefiltering an image using an opening or closing operation immensely improves maximum entropy thresholding. Examples of this combined technique are shown for both one- and two- dimensional entropic thresholding. The author points to this synergism as an example of the inherent interconnectedness of image processing and thresholding algorithms, and emphasizes the importance of the analysis of combined algorithms in the design of target detection and tracking schemes.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This communication presents an efficient hybrid nonlinear electromagnetic inversion method. The voxel-based variational Born iterative method (VBIM) is combined with the maximum entropy thresholding (MET) technique to reconstruct 3-D irregular scatterers embedded in the layered media. In each iteration, the VBIM first outputs the model parameters in discretized cells of the inversion domain, and the MET is then used to categorize them into “background” and “scatterer” types. Partial “background” cells will be removed in the next round iteration according to an adjustable threshold corresponding to the maximum entropy since they have no contribution to the measured scattered fields. Consequently, the inversion domain is gradually downsized in the successive iterations. The computation efficiency, memory cost, and the reconstruction accuracy are compared for VBIM with and without the MET. Their antinoise ability is also compared.

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.

As an important part of art and culture, ancient murals depict a variety of different artistic images, and these individual images have important research value. For research purposes, it is often important to first determine the type of objects represented in a painting. However, the mural painting environment makes datasets difficult to collect, and long-term exposure leads to underlying features that are not distinct, which makes this task challenging. This study proposes a convolutional neural network model based on the classic AlexNet network model and combines it with feature fusion to automatically classify ancient mural images. Due to the lack of large-scale mural datasets, the model first expands the dataset by applying image enhancement algorithms such as scaling, brightness conversion, noise addition, and flipping; then, it extracts the underlying features (such as fresco edges) shared by the first stage of a dual channel structure. Subsequently, a second-stage deep abstraction is conducted on the features extracted by the first stage using a two-channel network, each of which has a different structure. The obtained characteristics from both channels are merged, and a loss function is constructed to obtain the classification result. This approach improves the model's robustness and feature expression ability. The model achieves an accuracy of 84.24%, a recall rate of 84.15%, and an F1-measure of 84.13% when applied to a constructed mural image dataset. Compared with the AlexNet model and other improved convolutional neural network models, the proposed model improves each evaluation index by approximately 5%, verifying the rationality and effectiveness of the model for automatic mural image classification. The mural classification model proposed in this paper comprehensively considers the influences of network width and depth and can extract rich details from mural images from multiple local channels. An effective classification method could help researchers manage and protect mural images in an orderly fashion and quickly and effectively search for target images in a digital mural library based on a specified image category, aiding mural condition monitoring and restoration efforts as well as archaeological and art historical research.

Preparation of present article was aiming to try to answer the following questions: why the responsible business conduct in the Republic of Kazakhstan needs to include the protection of tangible cultural heritage. Authors consider legal aspects of protection of cultural heritage of Kazakhstan, its international obligations in it to the sphere and the status of objects of material cultural heritage in the territory of the Republic of Kazakhstan. At the level of UNESCO, the Republic of Kazakhstan has numerous official international obligations in protection of material cultural and natural heritage, which both the state and private organizations need to observe strictly. This system of the international obligations is complemented with the local legislation of the Republic of Kazakhstan directed to preservation of objects of cultural and natural heritage. Additionally, this work will list the basic recommendations, following of which is going to guarantee the protection and conservation of tangible cultural heritage sites, as well as save the representatives of the business sphere from various risks and associated financial losses related to the damaging and destruction of tangible cultural heritage sites. In addition to compliance with Kazakhstani and international legislative bases in the field of protection and use of tangible cultural heritage, the most socially responsible organizations can also organize their own events to raise awareness of their employees about the need to protect tangible cultural heritage.Key words: responsible business conduct, cultural heritage, protection, conservation, legislation.

PurposeThis study aims to investigate the performance analysis, science mapping and future direction of artificial intelligence (AI) technology, applications, tools and software used to preserve, curate and predict the historical value of cultural heritage.Design/methodology/approachThis study uses the bibliometric research method and utilizes the Scopus database to gather data. The keywords used are “artificial intelligence” and “cultural heritage,” resulting in 718 data sets spanning from 2001 to 2023. The data is restricted to the years 2001−2023, is in English language and encompasses all types of documents, including conference papers, articles, book chapters, lecture notes, reviews and editorials.FindingsThe performance analysis of research on the use of AI to aid in the preservation of cultural heritage has been ongoing since 2001, and research in this area continues to grow. The countries contributing to this research include Italy, China, Greece, Spain and the UK, with Italy being the most prolific in terms of authored works. The research primarily falls under the disciplines of computer science, mathematics, engineering, social sciences and arts and humanities, respectively. Document types mainly consist of articles and proceedings. In the science mapping process, five clusters have been identified. These clusters are labeled according to the contributions of AI tools, software, apps and technology to cultural heritage preservation. The clusters include “conservation assessment,” “exhibition and visualization,” “software solutions,” “virtual exhibition” and “metadata and database.” The future direction of research lies in extended reality, which integrates virtual reality (VR), augmented reality (AR) and mixed reality (MR); virtual restoration and preservation; 3D printing; as well as the utilization of robotics, drones and the Internet of Things (IoT) for mapping, conserving and monitoring historical sites and cultural heritage sites.Practical implicationsThe cultural heritage institution can use this result as a source to develop AI-based strategic planning for curating, preservation, preventing and presenting cultural heritages. Researchers and academicians will get insight and deeper understanding on the research trend and use the interdisciplinary of AI and cultural heritage for expanding collaboration.Social implicationsThis study will help to reveal the trend and evolution of AI and cultural heritage. The finding also will fill the knowledge gap on the research on AI and cultural heritage.Originality/valueSome similar bibliometric studies have been conducted; however, there are still limited studies on contribution of AI to preserve cultural heritage in wider view. The value of this study is the cluster in which AI is used to preserve, curate, present and assess cultural heritages.

Ancient murals are vulnerable to varying degrees of damage due to long-term exposure to external environmental factors such as light, temperature and humidity. Enabling people to appreciate the original features of murals has become a concern for field experts. The development of computer technology makes it possible to use intelligent information processing to simulate and restore ancient murals. This paper proposes an improved region growing algorithm based on threshold segmentation to automatically calibrate the flaking-related deterioration of murals in response to erosion by taking temple murals from the Song Dynasty in Kaihua Temple as the study object. First, we analyze the color characteristics of the flaking area, mark the suspected flaking-damaged points by threshold segmentation, and use these points as seeds for the area growth and expansion of the flaking area. We then calculate the color mask. Next, in the YCbCr and HSV color spaces, the brightness, chroma, and saturation characteristics of the flaking area are analyzed. The masks for the brightness, chroma, and saturation of the flaking area are obtained by threshold segmentation, and all the feature masks are merged. Finally, the mask of the flaking area obtained by data fusion is added to the original image to calibrate the flaking deterioration. Compared with current calibration algorithms based on multiscale mural deterioration, the experimental results show that the average error and error standard deviation of the proposed calibration algorithm are 1.91 and 1.82, respectively, without noise and 1.97 and 1.85, respectively, with noise. The errors are reduced, and the calibration performance is improved and stable. This work provides a good foundation for the virtual and practical restoration of ancient murals.

Considering the problems of low resolution and rough details in existing mural images, this paper proposes a superresolution reconstruction algorithm for enhancing artistic mural images, thereby optimizing mural images. The algorithm takes a generative adversarial network (GAN) as the framework. First, a convolutional neural network (CNN) is used to extract image feature information, and then, the features are mapped to the high-resolution image space of the same size as the original image. Finally, the reconstructed high-resolution image is output to complete the design of the generative network. Then, a CNN with deep and residual modules is used for image feature extraction to determine whether the output of the generative network is an authentic, high-resolution mural image. In detail, the depth of the network increases, the residual module is introduced, the batch standardization of the network convolution layer is deleted, and the subpixel convolution is used to realize upsampling. Additionally, a combination of multiple loss functions and staged construction of the network model is adopted to further optimize the mural image. A mural dataset is set up by the current team. Compared with several existing image superresolution algorithms, the peak signal-to-noise ratio (PSNR) of the proposed algorithm increases by an average of 1.2–3.3 dB and the structural similarity (SSIM) increases by 0.04 = 0.13; it is also superior to other algorithms in terms of subjective scoring. The proposed method in this study is effective in the superresolution reconstruction of mural images, which contributes to the further optimization of ancient mural images.

Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs

Ancient Chinese murals have a long history and a large number of types. They are witnesses to the development of ancient Chinese civilization. They still have important historical, artistic, and scientific values, as well as other values such as cultural relics, economy, and missionary education. Yet, the clear and smooth images of earlier have been damaged and covered by fog after the ancient murals were destroyed by human being and the nature. Therefore, the ancient murals that exist till now have been bothered by different damages and sickness. Protection and prevention are needed the most. In recent years, the application of scientific and technological means has played a role in the protection of ancient murals, making the work methods of cultural relics protection more scientific and diverse. In the context of the increasingly rich digital protection of cultural relics, the protection of murals requires more innovative work. However, at present, the resolution of ancient murals is low and the texture details are ambiguous, which leads to the problems of insufficient viewing and low research value. This paper focuses on ancient Chinese murals and conducts exploration on the phenomenon of mural damage and blurred colors. The deep learning intelligent laser scanning technology is used to extract the damaged ancient mural images. In this thesis, the images of murals have been restored using the new-super resolution technology to achieve the optimal mural images so that the mural images are more beautiful and artistic, which provides new ideas for the protection of murals.

The article deals with the protection of cultural heritage in wetlands under international agreements. It highlights the concept of the cultural and natural heritage of wetlands, and then discusses the protection of the world cultural heritage of wetlands within the framework of UNESCO. The article also identifies cultural values under the Ramsar Convention. It focuses on the assessment of international protection of world cultural and natural heritage in wetlands. Finally, the article refers to the World Heritage of the Wilderness Wetlands. The article contributes to clarifying the absence of an independent legal framework for the protection of cultural heritage in wetlands. The Study concluded that international agreements do not establish explicit international obligations on states. Hence the need for an international convention dedicated to the protection of cultural heritage in wetlands. The study also concluded that there is no judicial mechanism to limit the deterioration of cultural heritage in wetlands.

Метою статті є аналіз становлення та розвитку пам’яткоохоронного законодавства в Україні.
 Наукова новизна статті полягає в аналізі основних законодавчих і підзаконних актів України, завдяки яким відбулося реформування сфери охорони культурної спадщини в Україні та приведення її у відповідність до міжнародних норм і стандартів.
 Висновки. У законодавчому забезпеченні охорони культурної спадщини України можна до певної міри умовно виділити кілька етапів. Перший розпочався із прийняттям Декларації про державний суверенітет України від 16 липня 1990 р., яка започаткувала зміну державних підходів до культурної спадщини України, задекларувавши культурне відродження українського народу й необхідність повернення національних, культурних та історичних цінностей України, що знаходяться за її межами. Водночас базовим актом стали Основи законодавства України про культуру від 14 лютого 1992 р. Саме Основи законодавства України про культуру визначили правові засади діяльності органів публічної влади у сфері охорони культурної спадщини, а також започаткували процес формування Державного реєстру національного культурного надбання. Конституція України від 28 червня 1996 р. більш послідовно, у порівнянні з Конституцією УРСР, забезпечила гарантії культурних прав громадян та обов’язки держави щодо охорони культурної спадщини. На цьому етапі було ратифіковано ряд міжнародних актів у сфері охорони культурної спадщини, а також прийнято національні законодавчі й підзаконні акти, спрямовані на імплементацію цих норм у національне законодавство України, зокрема Закон України «Про вивезення, ввезення та повернення культурних цінностей» від 21 вересня 1999 р. Другий етап розпочався з ухваленням Закону України «Про охорону культурної спадщини» від 8 червня 2000 р., в якому враховані основні тенденції міжнародно-правового забезпечення охорони культурної спадщини, зокрема положення Конвенції ЮНЕСКО про охорону всесвітньої культурної і природної спадщини 1972 р. Законом урегульовані права та обов’язки суб’єктів охорони культурної спадщини, а також порядок формування Державного реєстру нерухомих пам’яток України за категоріями національного й місцевого значення. На цьому етапі ухвалені й такі важливі нормативні акти, як Закон України «Про охорону археологічної спадщини» від 18 березня 2004 р. і Загальнодержавна програма збереження та використання об’єктів культурної спадщини на 2004–2010 роки, затверджена Законом України від 20 квітня 2004 р. Третій етап розпочався з ухваленням Закону України «Про культуру» від 14 грудня 2010 р. і триває донині. На цьому етапі ухвалено ряд важливих змін до законодавства, спрямованих на приведення українського пам’яткоохоронного законодавства до міжнародних норм і стандартів, зокрема щодо збереження пам’яток культурної спадщини, включених до Списку всесвітньої спадщини ЮНЕСКО.

<p>Some countries have proven to be advanced by managing their historical and cultural heritage and promoting it internationally. Japan and South Korea are living examples, who transform the war site not only into national defense heritage but also bring it to the international level. The management of historical heritage is crucial to enhance people's awareness of the importance of national defense. However, many of Indonesia's historical relics are still neglected or poorly managed, even though many historical and cultural heritages have the potential to become tourist attractions. This article aims to discuss how to manage Indonesian historical and cultural heritage to enhance nationalism and patriotism. Proper management of historical and cultural heritage will increase the love of the motherland. The research method is done by a qualitative research method as well as literature studies. This study proves that the management of culture and historical heritage of Indonesia, especially those related to the national struggle, is still largely ignored. Moreover, Indonesia even lacks in managing its historical and cultural heritage. Therefore, Indonesia has to improve the management of its cultural and historical heritage so that it can be promoted to the global world as an object of tourism to increase the nationalism of the younger generation.</p>