Analysis of the Application of Artificial Intelligence Technology in the Digital Processing of Traditional Visual Art Elements

Abstract The emergence of image style migration technology provides a solution to the digitalization needs of traditional visual arts. In this paper, the traditional generative adversarial network model is improved and optimized, and a real-time image style migration algorithm is introduced, which separates the style and content and then optimizes them separately, and the processing speed is greatly improved. At the same time, considering that the native GAN discriminator is too powerful, and the problem of gradient disappearance may occur, leading to the collapse of the network, this paper designs an asymmetric loop-consistent generative adversarial network to improve and accelerate the optimization of the model. A saliency edge extraction module is embedded in this network, which is used to extract the saliency edge maps of the real image and the generated image of the traditional visual art elements, and this is used to calculate the saliency edge loss. The results of the comparison experiments show that the SSIM and PSNR values of the traditional visual art element images generated by this paper’s method are higher than those of the other four comparison methods, which proves the effectiveness of this paper’s model. From the immediate aesthetic analysis and multi-dimensional aesthetic analysis, the objective understanding of the audience’s aesthetic experience of traditional visual art elements is established, which puts forward effective suggestions on how to meet the audience’s needs and improve the audience’s experience of traditional visual art elements exhibitions.