Image Style Application in Art Design Based on GAN

The research aims to eliminate the defects of image style transfer algorithm in image quality, content consistency and style consistency, and improve the generalization ability. An improved GAN image style migration algorithm AMS-Cycle-GAN is designed and implemented. In this algorithm, the generator uses the position normalization and moment shortcut modules, and the discriminator is based on the GAN model with channel attention mechanism and spectral normalization. The results showed that AMS-Cycle-GAN showed good performance in improving the visual quality, content consistency and style consistency of the generated image through the experimental verification under various settings. Especially in the photo2vangogh and vangogh2photo datasets, the IS and FID values were significantly better than those of other methods, reaching 5.3±0.8 and 114.1, and 4.94±0.45 and 148.23, respectively. The improved design of AMS-cycle-GAN generator and discriminator proves its value as a reliable and superior image style migration algorithm with its excellent performance.