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"ANCIENT PAINTING"
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An introduction to Greek art : sculpture and vase painting in the archaic and classical periods
\"The four centuries between the composition of the Homeric epics and the conquests of Alexander the Great witnessed an immensely creative period in Greek art, one full of experimentation and innovation. But time has taken its toll; damaged statues have lost their colour and wall paintings have been totally destroyed. And yet sympathetic study of surviving sculpture and of drawing on vases can give extraordinary insight into and appreciation of these once brilliant works This book, designed originally for students, introduces the reader to Greek sculpture and vase painting in the critical period from the eighth to the fourth centuries BC. The works discussed are generously illustrated and lucidly analysed to give a vivid picture of the splendor of Greek art. The up-dated second edition includes a new chapter examining art in Greek society, a timeline to help relate artistic development to historical events, an explanation of how dates BC are arrived at, a brief overview of Greek temple plans and a further reading list of recent books. This clear, approachable and rigorous introduction makes the beauty of Greek art more readily accessible and comprehensible, balancing description with interpretation and illustration, and is an invaluable tool to help develop insight, appreciation and comprehension\"-- Provided by publisher.
Book
Virtual Restoration of Ancient Mold-Damaged Painting Based on 3D Convolutional Neural Network for Hyperspectral Image
Painted cultural relics hold significant historical value and are crucial in transmitting human culture. However, mold is a common issue for paper or silk-based relics, which not only affects their preservation and longevity but also conceals the texture, patterns, and color information, hindering cultural value and heritage. Currently, the virtual restoration of painting relics primarily involves filling in the RGB based on neighborhood information, which might cause color distortion and other problems. Another approach considers mold as noise and employs maximum noise separation for its removal; however, eliminating the mold components and implementing the inverse transformation often leads to more loss of information. To effectively acquire virtual restoration for mold removal from ancient paintings, the spectral characteristics of mold were analyzed. Based on the spectral features of mold and the cultural relic restoration philosophy of maintaining originality, a 3D CNN artifact restoration network was proposed. This network is capable of learning features in the near-infrared spectrum (NIR) and spatial dimensions to reconstruct the reflectance of visible spectrum, achieving the virtual restoration for mold removal of calligraphic and art relics. Using an ancient painting from the Qing Dynasty as a test subject, the proposed method was compared with the Inpainting, Criminisi, and inverse MNF transformation methods across three regions. Visual analysis, quantitative evaluation (the root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MEA), and a classification application were used to assess the restoration accuracy. The visual results and quantitative analyses demonstrated that the proposed 3D CNN method effectively removes or mitigates mold while restoring the artwork to its authentic color in various backgrounds. Furthermore, the color classification results indicated that the images restored with 3D CNN had the highest classification accuracy, with overall accuracies of 89.51%, 92.24%, and 93.63%, and Kappa coefficients of 0.88, 0.91, and 0.93, respectively. This research provides technological support for the digitalization and restoration of cultural artifacts, thereby contributing to the preservation and transmission of cultural heritage.
Journal Article
Ancient Painting Inpainting with Regional Attention-Style Transfer and Global Context Perception
Ancient paintings, as a vital component of cultural heritage, encapsulate a profound depth of cultural significance. Over time, they often suffer from different degradation conditions, leading to damage. Existing ancient painting inpainting methods struggle with semantic discontinuities, blurred textures, and details in missing areas. To address these issues, this paper proposes a generative adversarial network (GAN)-based ancient painting inpainting method named RG-GAN. Firstly, to address the inconsistency between the styles of missing and non-missing areas, this paper proposes a Regional Attention-Style Transfer Module (RASTM) to achieve complex style transfer while maintaining the authenticity of the content. Meanwhile, a multi-scale fusion generator (MFG) is proposed to use the multi-scale residual downsampling module to reduce the size of the feature map and effectively extract and integrate the features of different scales. Secondly, a multi-scale fusion mechanism leverages the Multi-scale Cross-layer Perception Module (MCPM) to enhance feature representation of filled areas to solve the semantic incoherence of the missing region of the image. Finally, the Global Context Perception Discriminator (GCPD) is proposed for the deficiencies in capturing detailed information, which enhances the information interaction across dimensions and improves the discriminator’s ability to identify specific spatial areas and extract critical detail information. Experiments on the ancient painting and ancient Huaniao++ datasets demonstrate that our method achieves the highest PSNR values of 34.62 and 23.46 and the lowest LPIPS values of 0.0507 and 0.0938, respectively.
Journal Article
Ancient Painting Inpainting Based on Multi-Layer Feature Enhancement and Frequency Perception
Image inpainting aims to restore the damaged information in images, enhancing their readability and usability. Ancient paintings, as a vital component of traditional art, convey profound cultural and artistic value, yet often suffer from various forms of damage over time. Existing ancient painting inpainting methods are insufficient in extracting deep semantic information, resulting in the loss of high-frequency detail features of the reconstructed image and inconsistency between global and local semantic information. To address these issues, this paper proposes a Generative Adversarial Network (GAN)-based ancient painting inpainting method using multi-layer feature enhancement and frequency perception, named MFGAN. Firstly, we design a Residual Pyramid Encoder (RPE), which fully extracts the deep semantic features of ancient painting images and strengthens the processing of image details by effectively combining the deep feature extraction module and channel attention. Secondly, we propose a Frequency-Aware Mechanism (FAM) to obtain the high-frequency perceptual features by using the frequency attention module, which captures the high-frequency details and texture features of the ancient paintings by increasing the skip connections between the low-frequency and the high-frequency features, and provides more frequency perception information. Thirdly, a Dual Discriminator (DD) is designed to ensure the consistency of semantic information between global and local region images, while reducing the discontinuity and blurring differences at the boundary during image inpainting. Finally, extensive experiments on the proposed ancient painting and Huaniao datasets show that our proposed method outperforms competitive image inpainting methods and exhibits robust generalization capabilities.
Journal Article
The imagery of the Athenian symposium
\"This book offers a new interpretation of sympotic scenes in sixth- and fifth-century BC Athenian vase painting. Through these images, the book explores what it meant to be a Greek community and how Athenians thought about past and present\"-- Provided by publisher.
Book
Results from the first phase of rock art survey in the Swaga Swaga Game Reserve (Tanzania)
Swaga Swaga is a game reserve located in the Kondoa region of Tanzania. Its northern borders are about 30 km in a straight line from the Irangi Hills, an area under UNESCO protection due to the presence of over 200 sites with rock art there (Fig. 1). Below, I present a short summary of field research conducted from April to June 2018 in Swaga Swaga. The research was carried out in order to locate potential new places with rock art.
Journal Article
Chinese Ancient Paintings Inpainting Based on Edge Guidance and Multi-Scale Residual Blocks
Chinese paintings have great cultural and artistic significance and are known for their delicate lines and rich textures. Unfortunately, many ancient paintings have been damaged due to historical and natural factors. The deep learning methods that are successful in restoring natural images cannot be applied to the inpainting of ancient paintings. Thus, we propose a model named Edge-MSGAN for inpainting Chinese ancient paintings based on edge guidance and multi-scale residual blocks. The Edge-MSGAN utilizes edge images to direct the completion network in order to generate entire ancient paintings. It then applies the multi-branch color correction network to adjust the colors. Furthermore, the model uses multi-scale channel attention residual blocks to learn the semantic features of ancient paintings at various levels. At the same time, by using polarized self-attention, the model can improve its concentration on significant structures, edges, and details, which leads to paintings that possess clear lines and intricate details. Finally, we have created a dataset for ancient paintings inpainting, and have conducted experiments in order to evaluate the model’s performance. After comparing the proposed model with state-of-the-art models from qualitative and quantitative aspects, it was found that our model is better at inpainting the texture, edge, and color of ancient paintings. Therefore, our model achieved maximum PSNR and SSIM values of 34.7127 and 0.9280 respectively, and minimum MSE and LPIPS values of 0.0006 and 0.0495, respectively.
Journal Article