Research on Digital Protection Algorithm of Cultural Heritage Based on Deep Learning
Kejia Zhu · 2025
This paper proposes a digital protection algorithm for cultural heritage based on deep learning, which combines the advantages of CNN and GAN, aiming to achieve high-quality restoration of historical heritage images and accurate reconstruction of three-dimensional models. First, in view of the common noise and defect problems of cultural heritage images, CNN is used for image denoising and restoration. Through multi-layer convolution feature extraction, image details are effectively restored, and the visual quality of the image is significantly improved. Then, GAN is used to produce high resolution images, which can improve the quality of the image and the quality of detail. In the 3D model reconstruction, a high precision 3D model is obtained by combining the deep learning algorithm and the traditional modeling technique. By optimizing the texture mapping algorithm, the realism and detail performance of the three-dimensional model are further improved, and the common texture blur and distortion problems in traditional methods are overcome. The experiment results indicate that the algorithm has a good performance in the restoration of the image, and the average PSNR and the structure similarity index (SSIM) are increased by 35%. In 3D modeling, the error of reconstruction model can be controlled within 1.5%, and the precision of texture mapping is improved obviously. Through comparative experiments with existing methods, the advantages and broad application prospects of the proposed algorithm in the digital protection of cultural heritage are verified.