IMAGE RESTORATION FOR HERITAGE PHOTOGRAPHY USING AI
DOI:
https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6636Keywords:
Heritage Photography, Image Restoration, Deep Learning, Gans, Diffusion Models, Cultural PreservationAbstract [English]
A significant portion of cultural and historical heritage is represented by photographic materials that are frequently damaged due to deterioration, poor storage or improper archival use. Traditional repair methods work to some extent, but are limited due to the need for physical work, analysis based on personal experience and processes that take a lot of time. Using the latest advancement of deep learning, this study proposes an AI-driven mechanism for old photos to be restored automatically. The study looks into the way to combine Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Diffusion Models to fix and enhance the damaged pictures. CNNs are used to get rid of noise and rebuild structures, GANs are used to create new textures and restore colours and Diffusion Models are used to improve fine-grained details and get rid of artefacts. The way they do it is getting the information from historical records and museums, primarily pictures, that have been damage d in various ways, such as faded, scratched, stained, and blurred. In order to prepare the information for the training of the models preprocessing techniques such as normalisation, damage segmentation and contrast reduction are applied. Frameworks such as TensorFlow and PyTorch are used in the application to create models and find the best parameters. To improve the modelling generalization and model stability, the normalisation of the dataset and the expansion of the data-set are employed. The proposed AI-based approach significantly improves the quality of repair over the standard digital approaches, as demonstrated by the results of experiments.
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Copyright (c) 2025 Dr. Vaishali Sandeep Baste, Mohit Malik, Dr. Parag Amin, Priya Dahiya, Mithhil Arora, Akhilesh Kalia
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