Artificial Intelligence-Based color Reconstruction of Mogao Grottoes Murals Using Computer Vision Techniques
DOI:
https://doi.org/10.22399/ijcesen.1555Keywords:
AI-driven restoration, Deep Learning, Historical pigments, Computer vision, Digital conservationAbstract
The Mogao Grottoes murals have deteriorated over centuries due to environmental exposure, pigment degradation, and natural ageing, making cultural heritage preservation difficult. AI and computer vision can identify, classify, and reconstruct faded pigments, revolutionizing color restoration. This reconstructs faded mural sections using deep learning, image processing, and pigment data implemented through TensorFlow, PyTorch and OpenCV. The study uses high-resolution Digital Dunhuang database images of Mogao Grottoes murals and 50 pigments categorized by color, stability, and chemical composition. CNNs and deep learning-based color mapping algorithms detect fading and suggest color restorations of pigments. AI reconstructions along with history accuracy through expert evaluations and pigment records. Artificial intelligence-driven mural conservation detects faded pigments, precisely reconstructs missing sections, and matches restored colors to historical authenticity, improving accuracy, efficiency, and scalability. Scientifically, AI-based digital heritage conservation outperforms manual restoration. AI preserves and faithfully reconstructs cultural heritage sites using historical artworks using global digital pigment database and deep learning-driven restoration models. The first reproducible and scientific model (CNN, GAN and deep learning-based color mapping algorithms) using AI-based color restoration and historical pigment analysis in Mogao Grottoes murals was created.
References
Kumar, V., Singh, R., Ahuja, I. P. S., & Hashmi, M. S. J. (2020). On technological solutions for repair and rehabilitation of heritage sites: a review. Advances in Materials and Processing Technologies, 6(1), 146–166.h https://doi.org/10.1080/2374068X.2020.1729044
Bentum, S. (2023). Digital transformation strategies for applied science domains. Indiana University. Research Ideas and Outcomes, 9, e105197.https://doi.org/10.3897/rio.9.e105197
Chekalina, V. (2024). The Impact of AI on the Illustration and Design Industries. [Doctoral dissertation, Universidade da Beira Interior]. Repositório Comum. Retrieved from https://comum.rcaap.pt/handle/10400.26/51140
Ghaith, K. (2024). Alhambra code: Decoding historical architecture through AI. Metaverse, 5(2), 2713. https://doi.org/10.54517/m.v5i2.2713
Nourbakhsh, I. R., & Keating, J. (2020). AI and Humanity. MIT press. Retrieved from: https://mitpress.mit.edu/9780262043892/ai-and-humanity/
Bhatti, D. S., Choi, Y., Wahidur, R. S. M., Bakhtawar, M., Kim, S., Lee, S., Lee, Y., & Lee, H.-N. (2025). AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution. ArXiv Preprint ArXiv:2502.06894. https://arxiv.org/abs/2502.06894
Radulescu, M., Dalal, S., Lilhore, U. K., & Saimiya, S. (2024). Optimizing mineral identification for sustainable resource extraction through hybrid deep learning enabled FinTech model. Resources Policy, 89, 104692. https://doi.org/10.1016/j.resourpol.2023.104692
Garcia-Moreno, F. M., Alcaraz, J. C., Rodríguez-Simón, L. R., & Hurtado-Torres, M. V. (2024). ARTDET: Machine learning software for automated detection of art deterioration in easel paintings. SoftwareX, 28, 101917.
Ashok, M., Rajeswaran, N., Atheeswaran, A., & VijayaRangan, V. (2024). Advanced Techniques in Medical Imaging: Computer Vision and Machine Learning Approaches. Quing Publications.
Núñez-Cacho, P., Mylonas, G., Kalogeras, A., & Molina-Moreno, V. (2024). Exploring the transformative power of AI in art through a circular economy lens. A systematic literature review. Heliyon, 10(4). https://doi.org/10.1016/j.heliyon.2024.e12345
Zhang, X. (2024). AI-Assisted Restoration of Yangshao Painted Pottery Using LoRA and Stable Diffusion. Heritage (2571-9408), 7(11).
Li, Z., Koban, K. C., Schenck, T. L., Giunta, R. E., Li, Q., & Sun, Y. (2022). Artificial intelligence in dermatology image analysis: current developments and future trends. Journal of Clinical Medicine, 11(22), 6826. https://doi.org/10.3390/jcm11226826
Ali, G., Mijwil, M. M., Adamopoulos, I., & Ayad, J. (2025). Leveraging the Internet of Things, Remote Sensing, and Artificial Intelligence for Sustainable Forest Management. Babylonian Journal of Internet of Things, 2025, 1–65.
Khalid, S., Azad, M. M., Kim, H. S., Yoon, Y., Lee, H., Choi, K.-S., & Yang, Y. (2024). A Review on Traditional and Artificial Intelligence-Based Preservation Techniques for Oil Painting Artworks. Gels, 10(8), 517. https://doi.org/10.1007/978-3-031-57746-8_14
Yeasmin, M. N., Al Amin, M., Joti, T. J., Aung, Z., & Azim, M. A. (2024). Advances of AI in image-based computer-aided diagnosis: A review. Array, 100357. https://doi.org/10.1016/j.array.2024.100357
Hutson, J. (2024). Combining Large Language Models and Immersive Technologies to Represent Cultural Heritage in the Metaverse Context. In Augmented and Virtual Reality in the Metaverse (pp. 265–281). Springer. https://doi.org/10.1007/978-3-031-57746-8_14
Sun, T., Jin, T., Huang, Y., Li, M., Wang, Y., Jia, Z., & Fu, X. (2024). Restoring dunhuang murals: crafting cultural heritage preservation knowledge into immersive virtual reality experience design. International Journal of Human–Computer Interaction, 40(8), 2019–2040. https://doi.org/10.1080/10447318.2024.1921234
Dredge, P., Howard, D., Morgan, K., & Yip, A. (2017). Unmasking Sidney Nolan’s Ned Kelly: X-ray Fluorescence Conservation Imaging, Art Historical Interpretation and Virtual Reality Visualisation. Australian and New Zealand Journal of Art, 17(2), 147–161.
Spennemann, D. H. R. (2024). Will artificial intelligence affect how cultural heritage will be managed in the future? Responses generated by Four genAI Models. Heritage, 7(3), 1453–1471. https://doi.org/10.3390/heritage7030080
Zhao, H., Hillson, N., Kleese van Dam, K., & Tanjore, D. (2022). Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges. U.S. Department of Energy Office of Science and Office of Energy Efficiency and Renewable Energy. https://doi.org/10.2172/1968870
Sirisathitkul, C., Sirisathitkul, Y., & Noonsuk, W. (2024). color analysis and classification of earthenware from Phra Mahathat Woramahawihan temple in southern Thailand using machine learning. STAR: Science & Technology of Archaeological Research, 10(1), 2359275. https://doi.org/10.1080/20548923.2024.2359275
Zhou, X., In, D., Xiong, X., Yang, K., Chen, X., Bruhn, H. M., Liu, X., & Yang, Y. (2023). A Note on Macroscopic Optical Coherence Tomography Imaging Enabled 3D Scanning for Museum and Cultural Heritage Applications. Journal of the American Institute for Conservation, 62(3), 178–187.
Balado, J., Olabarria, C., Martínez-Sánchez, J., Rodríguez-Pérez, J. R., & Pedro, A. (2021). Semantic segmentation of major macroalgae in coastal environments using high-resolution ground imagery and deep learning. International Journal of Remote Sensing, 42(5), 1785–1800.
Liu, H., Alli, H., & Yusoff, I. S. M. (2024). Genetic visualization of cultural heritage: A case study of Zhuxian Town woodblock prints. The Design Journal, 27(6), 1188–1207. https://doi.org/10.1080/14606925.2024.2397212
Tariq, R., Qureshi, I., Farooq, S., Ayoub, W. S., Akhter, S., Javaid, R., & Sultana, A. (2025). Artificial Intelligence in Cutting and Sorting of Fruits and Vegetables. In Artificial Intelligence in the Food Industry (pp. 134–152). CRC Press.
Yu, K. L., Ong, H. C., & Zaman, H. B. (2024). Integrated energy informatics technology on microalgae-based wastewater treatment to bioenergy production: A review. Journal of Environmental Management, 368, 122085.
Ha, N. T., Manley-Harris, M., Pham, T. D., & Hawes, I. (2021). The use of radar and optical satellite imagery combined with advanced machine learning and metaheuristic optimization techniques to detect and quantify above ground biomass of intertidal seagrass in a New Zealand estuary. International Journal of Remote Sensing, 42(12), 4712–4738.
Zhang, D., Xu, F., Wang, F., Le, L., & Pu, L. (2024). Synthetic biology and artificial intelligence in crop improvement. Plant Communications. https://doi.org/10.1016/j.xplc.2024.100356
Erekath, S., Seidlitz, H., Schreiner, M., & Dreyer, C. (2024). Food for future: Exploring cutting-edge technology and practices in vertical farm. Sustainable Cities and Society, 105357. https://doi.org/10.1016/j.scs.2024.105357
Forte, M. (2025). Exploring Multiverses: Generative AI and Neuroaesthetic Perspectives. Heritage, 8(3), 102. MDPI. Retrieved from https://www.mdpi.com/2571-9408/8/3/102
Namasivayam, V., Senguttuvan, N., Saravanan, V., Palaniappan, S., & Kathiravan, M. K. (2022). Artificial intelligence and its application in cardiovascular disease management. In Machine Learning and Systems Biology in Genomics and Health (pp. 189–236). Springer. https://doi.org/10.1007/978-981-16-5993-5_9
Sampson, D. M., & Sampson, D. D. (2024). AI-driven innovations in signal/image processing and data analysis for optical coherence tomography in clinical applications. In Biophotonics and Biosensing (pp. 417–480). Elsevier. https://doi.org/10.1016/B978-0-12-823647-5.00013-1
Chowdhury, M. E. H., Zughaier, S. M., Hasan, A., & Alfkey, R. (2024). Diabetic Foot Ulcers-Pathogenesis, Innovative Treatments and AI Applications: Pathogenesis, Innovative Treatments and AI Applications. BoD–Books on Demand.
Lai, C.-L., Karmakar, R., Mukundan, A., Natarajan, R. K., Lu, S.-C., Wang, C.-Y., & Wang, H.-C. (2024). Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review. APL Bioengineering, 8(4). https://doi.org/10.1063/5.0067925
Ruan, F., Zhang, T., & Li, H. (2019). Laser-induced breakdown spectroscopy in archeological science: a review of its application and future perspectives. Applied Spectroscopy Reviews, 54(7), 573–601. https://doi.org/10.1080/05704928.2019.1578753
Skiba, R. (2025). Advances in Cosmetology: Beauty Therapy Technology, Treatments, Sustainability, and Holistic Wellness. After Midnight Publishing. ISBN: 978-1-76384-409-4.
Wang, M. H., Xing, L., Pan, Y., Gu, F., Fang, J., Yu, X., Pang, C. P., Chong, K. K.-L., Cheung, C. Y.-L., & Liao, X. (2024). AI-based Advanced approaches and dry eye disease detection based on multi-source evidence: Cases, applications, issues, and future directions. Big Data Mining and Analytics, 7(2), 445–484. https://doi.org/10.26599/BDMA.2024.9020034
Petrak, S., Mahnić Naglić, M., Glogar, M., & Tomljenović, A. (2025). Assessment of Textile Material Properties and the Impact of Digital Ink-Jet Fabric Printing on 3D Simulation as a Sustainable Method for Garment Prototyping. Sustainability, 17(4), 1388. https://doi.org/10.3390/su17041388
Hafez, I. Y., & El-Mageed, A. A. A. (2025). Enhancing Digital Finance Security: AI-Based Approaches for Credit Card and Cryptocurrency Fraud Detection. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.21
M.K. Sarjas, & G. Velmurugan. (2025). Bibliometric Insight into Artificial Intelligence Application in Investment. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.864
P. Rathika, S. Yamunadevi, P. Ponni, V. Parthipan, & P. Anju. (2024). Developing an AI-Powered Interactive Virtual Tutor for Enhanced Learning Experiences. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.782
J. Prakash, R. Swathiramya, G. Balambigai, R. Menaha, & J.S. Abhirami. (2024). AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.780
V. Saravanan, Tripathi, K., K. N. S. K. Santhosh, Naveenkumar P., P. Vidyasri, & Bharathi Ramesh Kumar. (2025). AI-Driven Cybersecurity: Enhancing Threat Detection and Mitigation with Deep Learning. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1358
Ibeh, C. V., & Adegbola, A. (2025). AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.19
Olola, T. M., & Olatunde, T. I. (2025). Artificial Intelligence in Financial and Supply Chain Optimization: Predictive Analytics for Business Growth and Market Stability in The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.18
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
