A Generative Adversarial Network for Multistyle Unsupervised Image Transfer Based on Dynamic Position Normalization

Abstract

Image transfer is a technology that changes the image effect by processing the image’s color, contour, line, and other information. To stylize the visual appearance of the output will be adapted to the subject of the original image, this paper proposes an image feature transfer method called Cycle-DPN-GAN, based on the Cycle-Consistent Adversarial Networks. Firstly, Positional Normalization-Dynamic Moment Shortcut (PONO-DMS) module is introduced to learn more structural information from the input image, and the edge blurring and object losing are efficiently alleviated. In addition, the Multi-Scale-Structural Similarity Index (MS-SSIM) loss is added to the reconstruction loss, which improves visual perceptions and enhances the constraints on the reconstructed image in terms of image brightness, color contrast and structure. In this model, to verify the feasibility and superiority of the proposed method, the data sets of monet2photo, vangogh2photo, ukiyoe2photo and cezanne2photo are performed in the experiments, and the Inception Score and Fréchet Inception Distance evaluation index are improved. In addition, ablation studies are performed to demonstrate the validity of each proposed component. In this paper, the results of the quantitative evaluation are consistent with the qualitative evaluation. It can be demonstrated that the images generated by Cycle-DPN-GAN have higher visual quality.

Keywords

• CycleGAN
• deep learning
• generative adversarial network
• image transfer