Research on automatic generation and application of Ruyuan Yao embroidery based on self-attention mechanism
To address the limitations of current style migration models in processing Ruyuan Yao embroidery images,especially in effectively handling abstract geometric transformations and the high noise in the generated images,a style migration model for Ruyuan Yao embroidery named SA-CycleGAN was proposed.By incorporating a self-attention mechanism and replacing the objective function for generating the adversarial loss with WGAN,the model significantly enhanced its ability to capture the style features of Ruyuan Yao embroidery,thereby optimizing the quality of style mapping.In terms of application,the proposed SA-CycleGAN model not only provided solid technical support for the automatic generation and online design system of Ruyuan Yao embroidery patterns,but also facilitated the construction of the corresponding database and digital sharing platform.Rigorous comparative experiments demonstrated that the optimized SA-CycleGAN model achieved excellent performance in the evaluation indexes for Ruyuan Yao embroidery pattern factors,its FID value was reduced by 16.1%,and the IS value was relatively improved by 13.2%compared with the original CycleGAN model,resulting in significantly improved image quality that was visually closer to the original Ruyuan Yao embroidery style.The establishment of the pattern design system of Ruyuan Yao embroidery greatly enhanced the design efficiency,injecting new vigor and value into the preservation and innovation of the ethnic group patterns.
self-attention mechanismstyle transferRuyuan Yao embroiderypatterngenerative adversarial networkinteraction design