AI-BASED RESTORATION OF DIGITAL HERITAGE ARTWORKS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7073Keywords:
Digital Heritage Restoration, Cultural Heritage Preservation, AI-Based Restoration, Deep Learning, Generative Models, Image Inpainting, Cultural Authenticity, Expert-in-the-LoopAbstract [English]
Digitization of digital heritage artworks is of high importance to preserve cultural memory and provide access to historically important visual objects in the long run. The digitization of heritage materials is often affected by noise, fading, physical damage and gaps in the materials, all brought about by the physical decay of original materials and shortcomings in digitization processes by heritage institutions. The current paper presents a culturally sensitive AI-based restoration system that improves the visual quality and does not affect the stylistic integrity and historical authenticity. The model combines the use of convolutional neural networks to extract local features, generative models to reconstruct and restore severely damaged or missing areas and model global context using transformers to achieve compositional coherence. Cultural limitations and expert-in-the-loop validation is added to overcome ethical and authenticity issues so as to minimize the chances of over-restoration and style bashing. The proposed technique is tested on a variety of digital heritage collections such as paintings, manuscripts, murals, folk art and archival photographs. The results of experiments indicate that in comparison with traditional and baseline AI restoration methods, the experimental methods show consistent improvements in PSNR, SSIM, and LPIPS. Case analysis and expert evaluation also affirm that the framework offers an appropriate balance between visual addition and cultural conformity, which makes it appropriate to scalable and responsible preservation of digital heritage.
References
Bonazza, A., and Sardella, A. (2023). Climate Change and Cultural Heritage: Methods and Approaches for Damage and Risk Assessment. Heritage, 6, 3578-3589. https://doi.org/10.3390/heritage6040190 DOI: https://doi.org/10.3390/heritage6040190
Chindengwike, J. D. (2024). Application to Various Types of Computer Crimes and Digital Forensics: A Case of McCartney’s Net Worth and Settle. Journal of Digital Security and Forensics, 1(1), 30–33. https://doi.org/10.29121/digisecforensics.v1.i1.2024.20 DOI: https://doi.org/10.29121/digisecforensics.v1.i1.2024.20
Casillo, M., Colace, F., Gupta, B. B., Lorusso, A., Marongiu, F., and Santaniello, D. (2022). A Deep Learning Approach to Protecting Cultural Heritage Buildings Through IoT‑Based Systems. In Proceedings of the IEEE International Conference on Smart Computing (SMARTCOMP) (252-256). Helsinki, Finland. https://doi.org/10.1109/SMARTCOMP55677.2022.00063 DOI: https://doi.org/10.1109/SMARTCOMP55677.2022.00063
De Lucia, C., Amaddii, M., and Arrighi, C. (2024). Tangible and Intangible Ex Post Assessment of Flood‑Induced Damage to Cultural Heritage. Natural Hazards and Earth System Sciences, 24, 4317-4339. https://doi.org/10.5194/nhess-24-4317-2024 DOI: https://doi.org/10.5194/nhess-24-4317-2024
Goswami, A., Sharma, D., Mathuku, H., Gangadharan, S. M. P., Yadav, C. S., Sahu, S. K., Pradhan, M. K., Singh, J., and Imran, H. (2022). Change Detection in Remote Sensing Image Data Comparing Algebraic and Machine Learning Methods. Electronics, 11(3), 431. https://doi.org/10.3390/electronics11030431 DOI: https://doi.org/10.3390/electronics11030431
Jiang, T., Gan, X. E., Liang, Z., and Luo, G. (2022). AIDM: Artificial Intelligence for Digital Museum Autonomous Systems with Mixed Reality and Software‑Driven Data Analysis. Automated Software Engineering, 29, 22-45. https://doi.org/10.1007/s10515-021-00315-9 DOI: https://doi.org/10.1007/s10515-021-00315-9
Kharroubi, A., Poux, F., Ballouch, Z., Hajji, R., and Billen, R. (2022). Three‑Dimensional Change Detection Using Point Clouds: A Review. Geomatics, 2(4), 457-485. https://doi.org/10.3390/geomatics2040025 DOI: https://doi.org/10.3390/geomatics2040025
Kim, H., and Lee, H. (2022). Emotions and Colors in a Design Archiving System: Applying AI Technology for Museums. Applied Sciences, 12(3), 1467. https://doi.org/10.3390/app12052467 DOI: https://doi.org/10.3390/app12052467
Kosmas, P., Galanakis, G., Constantinou, V., Drossis, G., Christofi, M., Klironomos, I., Zaphiris, P., Antona, M., and Stephanidis, C. (2020). Enhancing Accessibility in Cultural Heritage Environments: Considerations for social computing. Universal Access in the Information Society, 19, 471-482. https://doi.org/10.1007/s10209-019-00651-4 DOI: https://doi.org/10.1007/s10209-019-00651-4
Li, Y., and Zhang, D. (2025). Toward Efficient Edge Detection Using Integral Image Optimization and Canny Edge Detection. Processes, 13(2), 293. https://doi.org/10.3390/pr13020293 DOI: https://doi.org/10.3390/pr13020293
Liu, S., Bovolo, F., Bruzzone, L., Du, Q., and Tong, X. (2021). Unsupervised Change Detection in Multitemporal Remote Sensing Images. In Change Detection and Image Time Series Analysis 1 (1-34). John Wiley & Sons. https://doi.org/10.1002/9781119882268.ch1 DOI: https://doi.org/10.1002/9781119882268.ch1
Moualla, A., Karaouzene, A., Boucenna, S., Vidal, D., and Gaussier, P. (2017). Readability of the gaze and Expressions Of a Robot Museum Visitor. In Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO‑MAN) (712-719). Lisbon, Portugal. https://doi.org/10.1109/ROMAN.2017.8172381 DOI: https://doi.org/10.1109/ROMAN.2017.8172381
Noh, H., Ju, J., Seo, M., Park, J., and Choi, D. G. (2022). Unsupervised Change Detection based on Image Reconstruction Loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (1351-1360). New Orleans, LA, USA. https://doi.org/10.1109/CVPRW56347.2022.00141 DOI: https://doi.org/10.1109/CVPRW56347.2022.00141
Sun, J., Khalid, K. A. T., and Kay, C. S. (2025). Deep Learning Models for Cultural Pattern Recognition to Preserve Intangible Heritage. Future Technologies, 4, 119-137. https://doi.org/10.55670/fpll.futech.4.3.12 DOI: https://doi.org/10.55670/fpll.futech.4.3.12
Zielke, T. (2020). Is Artificial Intelligence Ready for Standardization? In Proceedings of the European Conference on Systems, Software and Services Process Improvement (EuroSPI). Düsseldorf, Germany. https://doi.org/10.1007/978-3-030-56441-4_19 DOI: https://doi.org/10.1007/978-3-030-56441-4_19
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Copyright (c) 2026 Dharmesh Dhabliya, Nidhi Tewatia, Manjeet Natuk Gajbhiye, Shanthi V, Vinit Khetani, Dr. Gajanan P Arsalwad
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