Research on Computer Vision Restoration of Ancient Ceramic Images Based on the SinGAN Model

This paper proposes a computer vision-based image restoration method for ancient ceramics using the SinGAN model. Addressing the limitations of traditional methods, which rely on the subjective judgment of restoration experts to repair damaged ancient ceramic images, this study constructs a restoration model tailored to the distinctive features of ancient ceramics. By leveraging computer vision techniques generated by the model, the method effectively restores damaged ancient ceramic images.Through a hierarchical restoration mechanism based on image pyramids, the model optimizes the network by combining adversarial loss and reconstruction loss, and uses post-processing techniques to improve fusion. Experiments show that this method significantly outperforms traditional restoration methods that rely on the subjective experience of craftsmen in terms of restoration efficiency, accuracy, and visual effects. It can effectively restore the original appearance of ancient ceramics, providing new computer-based technical means for cultural heritage protection, achieving style transfer of missing parts of ancient ceramics, and ensuring visual consistency. The results indicate that the study provides new computer-based methods for the digital restoration of ancient ceramics. However, the model is highly dependent on data quality, and future improvements can be achieved by optimizing the network architecture, expanding the dataset, and integrating transfer learning techniques.