Research on the Application of Multi-Scale Feature Fusion GAN Framework in Vintage Photo Restoration and Colorization

Vintage photographs preserve precious historical memories but often suffer from damage and fading due to time erosion. This paper proposes a multi-scale feature fusion model based on Generative Adversarial Networks (GAN) for intelligent restoration and colorization of old photos. The model captures detailed information at different levels of old photos through a multi-scale feature extraction module, designs a fusion strategy to organically integrate local and global features, and enhances the authenticity of restored images via generative adversarial mechanisms. Experimental results show that compared with traditional methods, the model significantly improves in indicators such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), enabling better restoration of the original appearance of old photos. This study provides an effective solution for the digital protection and restoration of vintage photographs.