Unpaired image translation is a challenging problem in computer vision, while existing generative adversarial networks (GANs) models mainly use the adversarial loss and other constraints to model. But the degree of constraint imposed on the generator and the discriminator is not enough, which results in bad image quality. In addition, we find that the current GANs-based models have not yet been implemented by adding an auxiliary domain, which is used to constrain the generator. To solve the problem mentioned above, we propose a multiscale and multilevel GANs (MMGANs) model for image translation. In this model, we add an auxiliary domain to constrain generator, which combines this auxiliary domain with the original domains for modelling and helps generator learn the detailed content of the image. Then we use multiscale and multilevel feature matching to constrain the discriminator. The purpose is to make the training process as stable as possible. Finally, we conduct experiments on six image translation tasks. The results verify the validity of the proposed model.
Research on Image Translation and Image Quality Evaluation Based on Generative Adversarial Networks
