Application of Image Style Transfer Based on Normalized Residual Network in Art Design

With the development of computer vision technology, image style transfer technology based on deep learning has achieved vigorous development. It has been widely applied in fields such as art design, painting creation, and film and television effect production. However, existing image style transfer methods still have shortcomings, including low efficiency and weak quality of style transfer, which cannot better meet the actual needs of various art and design activities. Therefore, a residual network structure is introduced to construct an image style transfer model based on the convolutional neural networks. Meanwhile, a normalization layer is added to the residual network results to optimize the image style transfer technology. An image style transfer model based on the normalized residual network is constructed. The experimental results show that the accuracy, recall, and F1 values of the improved image style transfer model proposed in the study are 97.35%, 96.49%, and 97.52%, respectively, which can complete high-quality image style transfer. This indicates that the image style transfer model proposed in the study has good performance, which can effectively improve the efficiency and quality of image style transfer, providing effective support for various art and design activities.

@article{Pu2023,
  title     = {Application of Image Style Transfer Based on Normalized Residual Network in Art Design},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Jing Pu and Yuke Li},
  doi       = {10.14569/IJACSA.2023.0141005},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141005}
}