Artistic Image Style Transfer Based on Fusion Generative Adversarial Networks and VGG16
Nan Chen, Qingyong Meng, Lei Chen · IET Computers & Digital Techniques · 2026
Artistic image style transfer enables the synthesis of visually creative images by blending the structure of a content image with the textures of an artistic reference. However, existing models struggle to simultaneously preserve structural content and achieve coherent multi‐style fusion, often producing distorted outputs with limited controllability. To address these limitations, we propose Fusion GAN, a dual‐branch generative framework that integrates a content–style encoder, a multi‐style adaptive fusion block (MSAFB), and perceptual constraints derived from VGG16. The architecture enables controlled blending of multiple style statistics while maintaining spatial consistency. Unlike standard GAN‐based style transfer approaches, the proposed model incorporates a hybrid loss formulation combining perceptual content loss, Gram‐matrix‐based style loss, and adversarial realism regularization. The model is trained using MS‐COCO (content) and WikiArt (style) datasets under a fully reproducible experimental setup with fixed hyperparameters and repeated trials. An innovative image recognition approach for real‐time video surveillance is presented using a fusion generative adversarial network (fusion GAN). The method improves recognition robustness by separating structural features from appearance variations and adaptively fusing visual information under dynamic surveillance conditions. Quantitative results indicate partial improvements in structural retention but also highlight significant challenges, such as high Fréchet inception distance (FID) and low peak signal‐to‐noise ratio (PSNR)/structural similarity index (SSIM) values, demonstrating that the model remains an exploratory prototype rather than a high‐performance system. Qualitative outputs show successful stylistic blending, while the analysis identifies key failure points and areas for optimization. Overall, fusion GAN provides a structured and extensible foundation for multi‐style transfer, but further refinement is needed to achieve competitive realism and perceptual quality. The performance of the proposed fusion GAN model is evaluated using the MS‐COCO dataset for content images and the WikiArt dataset for artistic styles. Evaluation metrics such as FID, SSIM, and PSNR are employed to assess the model’s effectiveness in content preservation and style fidelity.