AI Driven Restoration of Damaged Paintings and Historical Artifacts Using Generative Adversarial Networks

Restoration of damaged art pieces, paintings, and artifacts is vital to the preservation of cultural heritage. Conventional techniques are done by hand, hence time-consuming and inconsistent. This paper proposes an AI-based restoration system with Generative Adversarial Networks (GANs) and Context Encoders to automate and improve the restoration process of digital artifacts. The process includes data collection, training, and model optimization to achieve good-quality outputs. The Context Encoder generates predictions for lost parts from adjacent image features, and the GAN model increases texture and color for full integration. The platform is easy to use with convenient uploads and rapid restoration. The model is assessed with quantitative metrics and expert judgment, showing better restoration accuracy. This AI-based solution is a productive and scalable alternative to human restoration and serves museums, historians, and cultural heritage.