An image style transfer algorithm based on attention mechanism and GhostNet

Abstract

Image style transfer, which combines the content of one image with the style of another to create a new image, has many potential applications in the disciplines of image creation, creative style reproduction, animation production, and other areas. The majority of early image style transfer techniques used manual features. Due to advances in deep learning technology, the Generative Adversarial Network (GAN) method greatly increases the accuracy of image style transfer; however, when it comes to scenes with large color blocks, high resolution, or horizontal boundaries, their performance still falls short of practical application requirements. In this research, we present a GhostNet- and attention-based image style transfer technique. We specifically introduce the SELayer to learn and increase the weight value of each channel, which can improve the quality of style transfer and data processing capacity. In addition, GhostNet is included to accelerate image production even more. The findings of the experiments, both quantitative and qualitative, demonstrate that the suggested technique can enhance the impact of style transfer.