Graphic design style transfer and aesthetic optimization algorithm based on a generative adversarial network

Current graphic design style transfer technology mainly focuses on geometric or texture features, while ignoring overall beauty and artistic expression, resulting in a mismatch between style and content, poor detail processing, and a lack of artistic appeal and true style presentation in the generated design. To this end, this article proposes a graphic design style transfer and aesthetic optimization algorithm based on a generative adversarial network (GAN). First, a graphic design image database with diverse styles is constructed; the GAN architecture is improved through generators, discriminators, and pre-trained VGG (visual geometry group) networks; an efficient channel attention mechanism and optimized inversion residual blocks are applied to enhance the model’s ability to capture aesthetic features; an aesthetic scoring model is designed by combining the loss functions of content, style, and generated images to ensure the visual appeal of generated images; VGG-19 (Visual geometry group-19) networks are used for pre-training, and the neural network parameters are optimized through the Adam algorithm to achieve efficient model training. The results show that the average values of SSIM and MSE (mean square error) for the improved GAN in this article are 0.93 and 0.027, respectively, in terms of content retention; the average value of MSE is 0.020 in terms of style similarity; the average values of PSNR (Peak Signal to Noise Ratio) and SSIM are 34.33 and 0.91 respectively in terms of image clarity. The study shows that the proposed method can not only improve the aesthetic quality and diversity of style transfer but also ensure the stability of image content, providing new theoretical and technical support for the field of graphic design style transfer.