Research on the Application of Classical Painting Restoration Based on Improved GAN Model

This study aims to advance image repair technology. By introducing an algorithm based on improved GAN, we design a novel generator network structure and an improved discriminator network to improve the clarity of generated results, the accuracy of edge structures, and reduce the time consumption of the repair process. The experimental results show that our algorithm significantly improves performance in a variety of image restoration tasks. Although the model performs well in handling classic paintings with different levels of damage and artistic styles, there are still challenges in handling special textures and severe damage. The improvements we suggest include experimenting with more advanced optimization algorithms and adding diverse training data. This study provides a valuable reference for researchers in the field of computer vision and image processing. We look forward to continuing to optimize algorithms and expand application scenarios in the future to make greater contributions to the development of image restoration technology.