AI-ASSISTED RESTORATION OF FOLK MURALS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6643Keywords:
AI Restoration, Folk Murals, Gan, Neural Style Transfer, Cultural Heritage PreservationAbstract [English]
Handmade techniques have been used for a long time in the restoration of folk paintings, which depict cultural stories and popular art. However, these traditional approaches to the restoration of artefacts are not always suitable for the modern demand for conservation. This is especially true in the event of significant damage, lack of records or loss of original colours. The rise of artificial intelligence (AI) has changed the way digital repair is done by making it possible for automatic rebuilding, colour improvement and texture creation to be done with amazing accuracy. This paper explores the use of advanced neural network architectures such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs) and Neural Style Transfer (NST) to assist in the restoration of folk paintings using AI. High-quality datasets were gathered by using different types of folk murals from different regions of the world. These datasets have been carefully preprocessed to remove noise, make colours more uniform and group them together. The AI models were trained to look for trends, fill in the blanks and copy the subtleties of style that are unique to folk customs. The test results indicate that the results are significantly superior to the ones obtained in regular digital techniques in both visual quality and the structure coherency. Competent art conservators have proven that the model is able to preserve the cultural identity while reducing the time and cost of restoration.
References
Budhavale, S., Desai, L. R., Paithane, S. A., Ajani, S. N., Singh, M., and Bhagat Patil, A. R. (2025). Elliptic Curve-Based Cryptography Solutions for Strengthening Network Security in IoT Environments. Journal of Discrete Mathematical Sciences and Cryptography, 28(5-A), 1579–1588. https://doi.org/10.47974/JDMSC-2156 DOI: https://doi.org/10.47974/JDMSC-2156
Chen, S., Vermol, V. V., and Ahmad, H. (2024). Exploring the Evolution and Challenges of Digital Media in the Cultural Value of Dunhuang Frescoes. Journal of Ecohumanism, 3, 1369–1376. https://doi.org/10.62754/joe.v3i5.3973 DOI: https://doi.org/10.62754/joe.v3i5.3973
Dahri, F. H., Abro, G. E. M., Dahri, N. A., Laghari, A. A., and Ali, Z. A. (2024). Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition. ICCK Transactions on Intelligent Systems, 2, 14–26. https://doi.org/10.62762/TIS.2025.613103 DOI: https://doi.org/10.62762/TIS.2025.613103
Feng, C.-Q., Li, B.-L., Liu, Y.-F., Zhang, F., Yue, Y., and Fan, J.-S. (2023). Crack Assessment Using Multi-Sensor Fusion Simultaneous Localization and Mapping (SLAM) and image super-resolution for bridge inspection. Automation in Construction, 155, Article 105047. https://doi.org/10.1016/j.autcon.2023.105047 DOI: https://doi.org/10.1016/j.autcon.2023.105047
Gao, Y., Li, W., Liang, S., Hao, A., and Tan, X. (2025). Saliency-Aware Foveated Path Tracing for Virtual Reality Rendering. IEEE Transactions on Visualization and Computer Graphics, 31, 2725–2735. https://doi.org/10.1109/TVCG.2025.3549148 DOI: https://doi.org/10.1109/TVCG.2025.3549148
Gao, Y., Zhang, Q., Wang, X., Huang, Y., Meng, F., and Tao, W. (2024). Multidimensional Knowledge Discovery of Cultural Relics Resources in the Tang Tomb Mural Category. The Electronic Library, 42, 1–22. https://doi.org/10.1108/EL-04-2023-0091 DOI: https://doi.org/10.1108/EL-04-2023-0091
Iqbal, M., Yousaf, J., Khan, A., and Muhammad, T. (2025). IoT-Enabled Food Freshness Detection Using Multi-Sensor Data Fusion and Mobile Sensing Interface. ICCK Transactions on Sensing, Communication and Control, 2, 122–131. https://doi.org/10.62762/TSCC.2025.401245 DOI: https://doi.org/10.62762/TSCC.2025.401245
Jaillant, L., Mitchell, O., Ewoh-Opu, E., and Hidalgo Urbaneja, M. (2025). How Can we Improve the Diversity of Archival Collections with AI? Opportunities, Risks, and Solutions. AI and Society, 40, 4447–4459. https://doi.org/10.1007/s00146-025-02222-z DOI: https://doi.org/10.1007/s00146-025-02222-z
Ma, W., Guan, Z., Wang, X., Yang, C., and Cao, J. (2023). YOLO-FL: A Target Detection Algorithm for Reflective Clothing Wearing Inspection. Displays, 80, Article 102561. https://doi.org/10.1016/j.displa.2023.102561 DOI: https://doi.org/10.1016/j.displa.2023.102561
Sun, H., Wang, Y., Du, J., and Wang, R. (2025). MFE-YOLO: A Multi-Feature Fusion Algorithm for Airport Bird Detection. ICCK Transactions on Intelligent Systems, 2, 85–94. https://doi.org/10.62762/TIS.2025.323887 DOI: https://doi.org/10.62762/TIS.2025.323887
Tekli, J. (2022). An Overview of Cluster-Based Image Search Result Organization: Background, Techniques, and Ongoing Challenges. Knowledge and Information Systems, 64, 589–642. https://doi.org/10.1007/s10115-021-01650-9 DOI: https://doi.org/10.1007/s10115-021-01650-9
Yu, T., Lin, C., Zhang, S., Wang, C., Ding, X., An, H., Liu, X., Qu, T., Wan, L., and You, S. (2022). Artificial Intelligence for Dunhuang Cultural Heritage Protection: The Project and the Dataset. International Journal of Computer Vision, 130, 2646–2673. https://doi.org/10.1007/s11263-022-01665-x DOI: https://doi.org/10.1007/s11263-022-01665-x
Zeng, Z., Qiu, S., Zhang, P., Tang, X., Li, S., Liu, X., and Hu, B. (2024). Virtual Restoration of Ancient Tomb Murals Based on Hyperspectral Imaging. Heritage Science, 12, Article 410. https://doi.org/10.1186/s40494-024-01501-0 DOI: https://doi.org/10.1186/s40494-024-01501-0
Zeng, Z., Sun, S., Li, T., Yin, J., and Shen, Y. (2022). Mobile Visual Search Model for Dunhuang Murals in the Smart Library. Library Hi Tech, 40, 1796–1818. https://doi.org/10.1108/LHT-03-2021-0079 DOI: https://doi.org/10.1108/LHT-03-2021-0079
Zhang, X. (2023). The Dunhuang Caves: Showcasing the Artistic Development and Social Interactions of Chinese Buddhism between the 4th and the 14th Centuries. Journal of Education, Humanities and Social Sciences, 21, 266–279. https://doi.org/10.54097/ehss.v21i.14016 DOI: https://doi.org/10.54097/ehss.v21i.14016
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Copyright (c) 2025 Anoop Dev, Sachin Singh, Ashmeet Kaur, Neha, Dr. Satish Upadhyay, Dr. J. Refonaa
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