Preserving historical images is crucial for maintaining our connection to the past, yet many of these invaluable records have deteriorated over time. Aging, environmental factors, and poor storage conditions lead to fading, scratches, and other damage that threaten the visual and historical integrity of these artifacts. This project aims to address these challenges by leveraging Generative Adversarial Networks (GANs) to restore and enhance historical images effectively.

The preservation of historical images is vital for maintaining cultural heritage, yet these images are often subject to significant degradation due to aging, environmental factors, and improper storage. This degradation poses several challenges for historians, archivists, and researchers:

• Visual Decay: Historical images frequently suffer from fading, discoloration, scratches, and physical damage, which diminishes their quality and obscures details critical for historical analysis.

• Loss of Context: Without restoration, the authentic visual representation and cultural context of these images are compromised, leading to a potential misinterpretation of historical events or settings.

• Labor-Intensive Restoration: Traditional restoration methods are time-consuming and require specialized expertise, making it difficult for institutions to restore large volumes of images efficiently.

• Limited Accessibility: Without scalable restoration, damaged historical images remain inaccessible or challenging to interpret for the general public, hindering their appreciation and educational value.